def __init__(self):
super(Parameter, self).__init__()
super(Utils, self).__init__()
super(FieldView, self).__init__()
super(Draw, self).__init__()
self.capture = cv2.VideoCapture(camera)
self.capture.set(cv2.CAP_PROP_FRAME_WIDTH,
self.width) # カメラ画像の横幅を1280に設定
self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT,
self.height) # カメラ画像の縦幅を720に設定
self.table_set = Tables()
self.planner = PathPlanning(send=self.send)
self.table_detection = True
self.detection_success = False
self.bottle_result = [None, None, None]
self.standing_result_image = None
self.quit = False
self.yukari = Yukari()
self.remove_separator_middle = False
self.use_remove_separator = True
self.points = None
self.flip_points = None
self.set_track_bar_pos(self.settings)
self.ptlist = PointList(NPOINTS)
self.click_mode = True
logging.info('START DETECTION')
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.target_selection = TargetSelection()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.inner_target_exists = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# ROS Publisher for the path
self.path_publisher = rospy.Publisher(rospy.get_param('path_pub_topic'), \
Path, queue_size = 10)
# ROS Publisher for the subtargets
self.subtargets_publisher = rospy.Publisher(rospy.get_param('subgoals_pub_topic'),\
MarkerArray, queue_size = 10)
# ROS Publisher for the current target
self.current_target_publisher = rospy.Publisher(rospy.get_param('curr_target_pub_topic'),\
Marker, queue_size = 10)
def __init__(self, selection_method):
self.goals_position = []
self.goals_value = []
self.omega = 0.0
self.radius = 0
self.method = selection_method
self.brush = Brushfires()
self.topo = Topology()
self.path_planning = PathPlanning()
def __init__(self, selection_method):
self.goals_position = []
self.goals_value = []
self.omega = 0.0
self.radius = 0
self.method = selection_method
self.brush = Brushfires()
self.topo = Topology()
self.path_planning = PathPlanning()
# Initialize previous target
self.previous_target = [-1, -1]
def __init__(self, selection_method):
self.goals_position = []
self.goals_value = []
self.path = []
self.prev_target = [0, 0]
self.omega = 0.0
self.radius = 0
self.method = selection_method
self.brush = Brushfires()
self.topo = Topology()
self.path_planning = PathPlanning()
self.robot_perception = RobotPerception() # can i use that?
def __init__(self, selection_method):
self.goals_position = []
self.goals_value = []
self.omega = 0.0
self.radius = 0
self.method = selection_method
if self.method_is_cost_based():
from robot_perception import RobotPerception
self.robot_perception = RobotPerception()
self.cost_based_properties = rospy.get_param("cost_based_properties")
numpy.set_printoptions(precision=3, threshold=numpy.nan, suppress=True)
self.brush = Brushfires()
self.path_planning = PathPlanning()
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.select_another_target = 0
self.inner_target_exists = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
self.count_limit = 200 # 20 sec
self.counter_to_next_sub = self.count_limit
self.laser_aggregation = LaserDataAggregator()
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# Read the target function
self.target_selector = rospy.get_param("target_selector")
print "The selected target function is" + self.target_selector
self.target_selection = TargetSelection(self.target_selector)
# ROS Publisher for the path
self.path_publisher = \
rospy.Publisher(rospy.get_param('path_pub_topic'), \
Path, queue_size = 10)
# ROS Publisher for the subtargets
self.subtargets_publisher = \
rospy.Publisher(rospy.get_param('subgoals_pub_topic'),\
MarkerArray, queue_size = 10)
# ROS Publisher for the current target
self.current_target_publisher = \
rospy.Publisher(rospy.get_param('curr_target_pub_topic'),\
Marker, queue_size = 10)
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.target_selection = TargetSelection()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.inner_target_exists = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# ROS Publisher for the path
self.path_publisher = rospy.Publisher(rospy.get_param('path_pub_topic'), \
Path, queue_size = 10)
# ROS Publisher for the subtargets
self.subtargets_publisher = rospy.Publisher(rospy.get_param('subgoals_pub_topic'),\
MarkerArray, queue_size = 10)
# ROS Publisher for the current target
self.current_target_publisher = rospy.Publisher(rospy.get_param('curr_target_pub_topic'),\
Marker, queue_size = 10)
def __init__(self, selection_method):
self.initial_time = time()
self.method = selection_method
self.initialize_gains = False
self.brush = Brushfires()
self.topology = Topology()
self.path_planning = PathPlanning()
self.droneConnector = DroneCommunication()
# Parameters from YAML File
self.debug = True #rospy.get_param('debug')
self.map_discovery_purpose = rospy.get_param('map_discovery_purpose')
self.color_evaluation_flag = rospy.get_param('color_rating')
self.drone_color_evaluation_topic = rospy.get_param(
'drone_pub_color_rating')
self.evaluate_potential_targets_srv_name = rospy.get_param(
'rate_potential_targets_srv')
# Explore Gains
self.g_color = 0.0
self.g_brush = 0.0
self.g_corner = 0.0
self.g_distance = 0.0
self.set_gain()
if self.color_evaluation_flag:
# Color Evaluation Service
self.color_evaluation_service = rospy.ServiceProxy(
self.evaluate_potential_targets_srv_name, EvaluateTargets)
# Subscribe to Color Evaluation Topic to Get Results from Color Evaluation
self.drone_color_evaluation_sub = rospy.Subscriber(
self.drone_color_evaluation_topic, ColorEvaluationArray,
self.color_evaluation_cb)
# Parameters
self.targets_color_evaluated = False # Set True Once Color Evaluation of Targets Completed
self.color_evaluation = [] # Holds the Color Evaluation of Targets
self.corner_evaluation = [
] # Holds the Number of Corners Near Each Target
def __init__(self, selection_method):
self.goals_position = []
self.goals_value = []
self.omega = 0.0
self.radius = 0
self.method = selection_method
self.previous_target = []
self.brush = Brushfires()
self.topo = Topology()
self.path_planning = PathPlanning()
self.previous_target.append(50)
self.previous_target.append(50)
self.node2_index_x = 0
self.node2_index_y = 0
self.sonar = SonarDataAggregator()
self.timeout_happened = 0
import cv2
import numpy as np
from realsense.view import FieldView
from path_planning import PathPlanning
def f(x):
return x - 1250
planner = PathPlanning(send=False)
size = 720, 1280, 3
v = FieldView()
window_name = 'main'
cv2.namedWindow(window_name)
cv2.createTrackbar('table_1', window_name, f(1250), f(3750), int)
cv2.createTrackbar('table_2', window_name, f(1250), f(3750), int)
cv2.createTrackbar('table_3', window_name, f(1250), f(3750), int)
cv2.createTrackbar('table1_result', window_name, 0, 1, int)
cv2.createTrackbar('table2_result', window_name, 0, 1, int)
cv2.createTrackbar('table3_result', window_name, 0, 1, int)
cv2.createTrackbar('zone', window_name, 0, 1, int)
cv2.setTrackbarPos('table_1', window_name, f(2500))
cv2.setTrackbarPos('table_2', window_name, f(2500))
cv2.setTrackbarPos('table_3', window_name, f(2500))
while True:
pos1 = cv2.getTrackbarPos('table_1', window_name) + 1250
pos2 = cv2.getTrackbarPos('table_2', window_name) + 1250
pos3 = cv2.getTrackbarPos('table_3', window_name) + 1250
results = [
cv2.getTrackbarPos('table1_result', window_name),
class Navigation:
# Constructor
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.select_another_target = 0
self.inner_target_exists = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
self.time_limit = 20 # seconds
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# Read the target function
self.target_selector = rospy.get_param("target_selector")
print "The selected target function is " + self.target_selector
self.target_selection = TargetSelection(self.target_selector)
# ROS Publisher for the path
self.path_publisher = \
rospy.Publisher(rospy.get_param('path_pub_topic'), \
Path, queue_size = 10)
# ROS Publisher for the subtargets
self.subtargets_publisher = \
rospy.Publisher(rospy.get_param('subgoals_pub_topic'),\
MarkerArray, queue_size = 10)
# ROS Publisher for the current target
self.current_target_publisher = \
rospy.Publisher(rospy.get_param('curr_target_pub_topic'),\
Marker, queue_size = 10)
# Timer Thread (initialization)
self.timerThread = TimerThread(self.time_limit)
def checkTarget(self, event):
# Check if we have a target or if the robot just wanders
if self.inner_target_exists == False or self.move_with_target == False or\
self.next_subtarget == len(self.subtargets):
return
# Check if timer has expired
if self.timerThread.expired == True:
Print.art_print('\n~~~~ Time reset ~~~~',Print.RED)
self.inner_target_exists = False
self.target_exists = False
return
# Get the robot pose in pixels
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
theta_robot = self.robot_perception.robot_pose['th']
# Clear achieved targets
self.subtargets = self.subtargets[self.next_subtarget:]
self.next_subtarget = 0
# Find the distance between the robot pose and all remaining subtargets
dx = [rx - st[0] for st in self.subtargets]
dy = [ry - st[1] for st in self.subtargets]
dist = [math.hypot(v[0], v[1]) for v in zip(dx, dy)]
# Try to optimize path only when there is more than one target available
if len(self.subtargets) > 1:
ogm = self.robot_perception.getMap()
theta_goals = np.arctan2(-np.array(dy),-np.array(dx))
dtheta = theta_goals - theta_robot
dtheta[dtheta > np.pi] -= 2*np.pi
dtheta[dtheta < -np.pi] += 2*np.pi
# Check if there are obstacles between robot and subtarget
obstacles_subtarget = []
for st,i in zip(self.subtargets, range(len(self.subtargets))):
# Find line in pixels between robot and goal
line_pxls = list(bresenham(int(round(st[0])), int(round(st[1])),\
int(round(rx)), int(round(ry))))
# Find if there are any obstacles in line
ogm_line = list(map(lambda pxl: ogm[pxl[0], pxl[1]], line_pxls))
N_occupied = len(list(filter(lambda x: x > 80, ogm_line)))
obstacles_subtarget.append(N_occupied)
# Check if one of next goals is aligned with the current robot theta
# In this case plan immediately for this goal in order to improve motion behaviour
for st, i in zip(self.subtargets, range(len(self.subtargets))):
if np.fabs(dtheta[i]) < np.pi/10 and obstacles_subtarget[i] == 0:
self.next_subtarget = i
# Keep resetting timer, while moving towards target (without any obstacles inbetween)
self.timerThread.reset()
# Check if any target is in distance
min_dist, min_idx = min(zip(dist, range(len(dist))))
if min_dist < 5:
self.next_subtarget = min_idx + 1
# Reset timer if a goal has been reached
self.timerThread.reset()
# Check if the final subtarget has been approached
if self.next_subtarget >= len(self.subtargets):
self.target_exists = False
# Publish the current target
if self.next_subtarget >= len(self.subtargets):
return
subtarget = [\
self.subtargets[self.next_subtarget][0]\
* self.robot_perception.resolution + \
self.robot_perception.origin['x'],
self.subtargets[self.next_subtarget][1]\
* self.robot_perception.resolution + \
self.robot_perception.origin['y']\
]
RvizHandler.printMarker(\
[subtarget],\
1, # Type: Arrow
0, # Action: Add
"map", # Frame
"art_next_subtarget", # Namespace
[0, 0, 0.8, 0.8], # Color RGBA
0.2 # Scale
)
# Function that selects the next target, produces the path and updates
# the coverage field. This is called from the speeds assignment code, since
# it contains timer callbacks
def selectTarget(self):
# Cancel previous goal timer
self.timerThread.stop()
# IMPORTANT: The robot must be stopped if you call this function until
# it is over
# Check if we have a map
while self.robot_perception.have_map == False:
Print.art_print("Navigation: No map yet", Print.RED)
return
print "\nClearing all markers"
RvizHandler.printMarker(\
[[0, 0]],\
1, # Type: Arrow
3, # Action: delete all
"map", # Frame
"null", # Namespace
[0,0,0,0], # Color RGBA
0.1 # Scale
)
print '\n\n----------------------------------------------------------'
print "Navigation: Producing new target"
# We are good to continue the exploration
# Make this true in order not to call it again from the speeds assignment
self.target_exists = True
# Gets copies of the map and coverage
local_ogm = self.robot_perception.getMap()
local_ros_ogm = self.robot_perception.getRosMap()
local_coverage = self.robot_perception.getCoverage()
print "Got the map and Coverage"
self.path_planning.setMap(local_ros_ogm)
# Once the target has been found, find the path to it
# Get the global robot pose
g_robot_pose = self.robot_perception.getGlobalCoordinates(\
[self.robot_perception.robot_pose['x_px'],\
self.robot_perception.robot_pose['y_px']])
# Call the target selection function to select the next best goal
# Choose target function
self.path = []
force_random = False
# Potential Target
path_altered = False
previous_target = None
while len(self.path) == 0:
start = time.time()
target = self.target_selection.selectTarget(\
local_ogm,\
local_coverage,\
self.robot_perception.robot_pose,
self.robot_perception.origin,
self.robot_perception.resolution,
force_random)
# ipdb.set_trace()
# Check if there was any path alteration
if target[0] == True:
previous_target = target[2]
target = target[1]
path_altered = True
else:
target = target[1]
self.path = self.path_planning.createPath(\
g_robot_pose,\
target,
self.robot_perception.resolution)
print "Navigation: Path for target found with " + str(len(self.path)) +\
" points"
if len(self.path) == 0:
Print.art_print(\
"Path planning failed. Fallback to random target selection", \
Print.RED)
force_random = True
# Reverse the path to start from the robot
self.path = self.path[::-1]
# Break the path to subgoals every 2 pixels (1m = 20px)
step = 1
n_subgoals = (int) (len(self.path)/step)
self.subtargets = []
for i in range(0, n_subgoals):
self.subtargets.append(self.path[i * step])
self.subtargets.append(self.path[-1])
self.next_subtarget = 0
print "The path produced " + str(len(self.subtargets)) + " subgoals"
######################### NOTE: QUESTION ##############################
# The path is produced by an A* algorithm. This means that it is
# optimal in length but 1) not smooth and 2) length optimality
# may not be desired for coverage-based exploration
########################################################################
# Start timer thread
self.timerThread.start()
# Publish the path for visualization purposes
ros_path = Path()
ros_path.header.frame_id = "map"
for p in self.path:
ps = PoseStamped()
ps.header.frame_id = "map"
ps.pose.position.x = 0
ps.pose.position.y = 0
######################### NOTE: QUESTION ##############################
# Fill the ps.pose.position values to show the path in RViz
# You must understand what self.robot_perception.resolution
# and self.robot_perception.origin are.
# ipdb.set_trace()
ps.pose.position.x = p[0] * self.robot_perception.resolution \
+ self.robot_perception.origin['x']
ps.pose.position.y = p[1] * self.robot_perception.resolution \
+ self.robot_perception.origin['y']
########################################################################
ros_path.poses.append(ps)
self.path_publisher.publish(ros_path)
# Publish the subtargets for visualization purposes
subtargets_mark = []
for s in self.subtargets:
subt = [
s[0] * self.robot_perception.resolution + \
self.robot_perception.origin['x'],
s[1] * self.robot_perception.resolution + \
self.robot_perception.origin['y']
]
subtargets_mark.append(subt)
RvizHandler.printMarker(\
subtargets_mark,\
2, # Type: Sphere
0, # Action: Add
"map", # Frame
"art_subtargets", # Namespace
[0, 0.8, 0.0, 0.8], # Color RGBA
0.2 # Scale
)
# Print Old and Current Final Target
if path_altered == True:
subt = [
previous_target[0] * self.robot_perception.resolution + \
self.robot_perception.origin['x'],
previous_target[1] * self.robot_perception.resolution + \
self.robot_perception.origin['y']
]
# ipdb.set_trace()
RvizHandler.printMarker(\
[subt],\
3, # Type: Sphere
0, # Action: Add
"map", # Frame
"art_subtargets", # Namespace
[1.0, 0.0, 0.0, 0.8], # Color RGBA
0.2 # Scale
)
# ipdb.set_trace()
self.inner_target_exists = True
def velocitiesToNextSubtarget(self):
[linear, angular] = [0, 0]
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
theta = self.robot_perception.robot_pose['th']
######################### NOTE: QUESTION ##############################
# The velocities of the robot regarding the next subtarget should be
# computed. The known parameters are the robot pose [x,y,th] from
# robot_perception and the next_subtarget [x,y]. From these, you can
# compute the robot velocities for the vehicle to approach the target.
# Hint: Trigonometry is required
l_max = 0.3
a_max = 0.3
if self.subtargets and self.next_subtarget <= len(self.subtargets) - 1:
st_x = self.subtargets[self.next_subtarget][0]
st_y = self.subtargets[self.next_subtarget][1]
theta_rg = np.arctan2(st_y - ry, st_x - rx)
dtheta = (theta_rg - theta)
if dtheta > np.pi:
omega = (dtheta - 2*np.pi)/np.pi
elif dtheta < -np.pi:
omega = (dtheta + 2*np.pi)/np.pi
else:
omega = (theta_rg - theta)/np.pi
# Nonlinear relations derived from experimentation
linear = l_max * ((1 - np.abs(omega)) ** 5)
angular = a_max * np.sign(omega) * (abs(omega) ** (1/5))
######################### NOTE: QUESTION ##############################
return [linear, angular]
class Navigation:
# Constructor
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.select_another_target = 0
self.inner_target_exists = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
self.count_limit = 200 # 20 sec
self.counter_to_next_sub = self.count_limit
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# Read the target function
self.target_selector = rospy.get_param("target_selector")
print "The selected target function is " + self.target_selector
self.target_selection = TargetSelection(self.target_selector)
# ROS Publisher for the path
self.path_publisher = \
rospy.Publisher(rospy.get_param('path_pub_topic'), \
Path, queue_size = 10)
# ROS Publisher for the subtargets
self.subtargets_publisher = \
rospy.Publisher(rospy.get_param('subgoals_pub_topic'),\
MarkerArray, queue_size = 10)
# ROS Publisher for the current target
self.current_target_publisher = \
rospy.Publisher(rospy.get_param('curr_target_pub_topic'),\
Marker, queue_size = 10)
def checkTarget(self, event):
# Check if we have a target or if the robot just wanders
if self.inner_target_exists == False or self.move_with_target == False or\
self.next_subtarget == len(self.subtargets):
return
self.counter_to_next_sub -= 1
if self.counter_to_next_sub == 0:
Print.art_print('\n~~~~ Time reset ~~~~', Print.RED)
self.inner_target_exists = False
self.target_exists = False
return
# Get the robot pose in pixels
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
# Find the distance between the robot pose and the next subtarget
dist = math.hypot(\
rx - self.subtargets[self.next_subtarget][0], \
ry - self.subtargets[self.next_subtarget][1])
######################### NOTE: QUESTION ##############################
# What if a later subtarget or the end has been reached before the
# next subtarget? Alter the code accordingly.
# If the robot is in the starting point it immediately sets the next subtarget
if self.next_subtarget == 0:
self.next_subtarget += 1
self.counter_to_next_sub = self.count_limit
else:
# Check if there is a later subtarget, closer than the next one
# If one is found, make it the next subtarget and update the time
for i in range(self.next_subtarget + 1, len(self.subtargets) - 1):
# Find the distance between the robot pose and the later subtarget
dist_from_later = math.hypot(\
rx - self.subtargets[i][0], \
ry - self.subtargets[i][1])
if dist_from_later < 15:
self.next_subtarget = i
self.counter_to_next_sub = self.count_limit + 100
dist = dist_from_later
break
# If distance from subtarget is less than 5, go to the next one
if dist < 5:
self.next_subtarget += 1
self.counter_to_next_sub = self.count_limit
# Check if the final subtarget has been approached
if self.next_subtarget == len(self.subtargets):
self.target_exists = False
########################################################################
# Publish the current target
if self.next_subtarget == len(self.subtargets):
return
subtarget = [\
self.subtargets[self.next_subtarget][0]\
* self.robot_perception.resolution + \
self.robot_perception.origin['x'],
self.subtargets[self.next_subtarget][1]\
* self.robot_perception.resolution + \
self.robot_perception.origin['y']\
]
RvizHandler.printMarker(\
[subtarget],\
1, # Type: Arrow
0, # Action: Add
"map", # Frame
"art_next_subtarget", # Namespace
[0, 0, 0.8, 0.8], # Color RGBA
0.2 # Scale
)
# Function that selects the next target, produces the path and updates
# the coverage field. This is called from the speeds assignment code, since
# it contains timer callbacks
def selectTarget(self):
# IMPORTANT: The robot must be stopped if you call this function until
# it is over
# Check if we have a map
while self.robot_perception.have_map == False:
Print.art_print("Navigation: No map yet", Print.RED)
return
print "\nClearing all markers"
RvizHandler.printMarker(\
[[0, 0]],\
1, # Type: Arrow
3, # Action: delete all
"map", # Frame
"null", # Namespace
[0,0,0,0], # Color RGBA
0.1 # Scale
)
print '\n\n----------------------------------------------------------'
print "Navigation: Producing new target"
# We are good to continue the exploration
# Make this true in order not to call it again from the speeds assignment
self.target_exists = True
# Gets copies of the map and coverage
local_ogm = self.robot_perception.getMap()
local_ros_ogm = self.robot_perception.getRosMap()
local_coverage = self.robot_perception.getCoverage()
print "Got the map and Coverage"
self.path_planning.setMap(local_ros_ogm)
# Once the target has been found, find the path to it
# Get the global robot pose
g_robot_pose = self.robot_perception.getGlobalCoordinates(\
[self.robot_perception.robot_pose['x_px'],\
self.robot_perception.robot_pose['y_px']])
# Call the target selection function to select the next best goal
# Choose target function
self.path = []
force_random = False
while len(self.path) == 0:
start = time.time()
target = self.target_selection.selectTarget(\
local_ogm,\
local_coverage,\
self.robot_perception.robot_pose,
self.robot_perception.origin,
self.robot_perception.resolution,
force_random)
self.path = self.path_planning.createPath(\
g_robot_pose,\
target,
self.robot_perception.resolution)
print "Navigation: Path for target found with " + str(len(self.path)) +\
" points"
if len(self.path) == 0:
Print.art_print(\
"Path planning failed. Fallback to random target selection", \
Print.RED)
force_random = True
# Reverse the path to start from the robot
self.path = self.path[::-1]
######################### NOTE: QUESTION ##############################
# The path is produced by an A* algorithm. This means that it is
# optimal in length but 1) not smooth and 2) length optimality
# may not be desired for coverage-based exploration
weight_data = 0.5 # How much weight to update the data (a)
weight_smooth = 0.1 # How much weight to smooth the coordinates (b)
min_change = 0.0001 # Minimum change per iteration to keep iterating
new_path = np.copy(np.array(self.path))
path_length = len(self.path[0])
change = min_change
while change >= min_change:
change = 0.0
for i in range(1, len(new_path) - 1):
for j in range(path_length):
# Initialize x, y
x_i = self.path[i][j]
y_i = new_path[i][j]
y_prev = new_path[i - 1][j]
y_next = new_path[i + 1][j]
y_i_saved = y_i
# Minimize the distance between coordinates of the original
# path (y) and the smoothed path (x). Also minimize the
# difference between the coordinates of the smoothed path
# at time step i, and neighboring time steps. In order to do
# all the minimizations, we use Gradient Ascent.
y_i += weight_data * (x_i - y_i) + weight_smooth * (
y_next + y_prev - (2 * y_i))
new_path[i][j] = y_i
change += abs(y_i - y_i_saved)
self.path = new_path
########################################################################
# Break the path to subgoals every 2 pixels (1m = 20px)
step = 1
n_subgoals = (int)(len(self.path) / step)
self.subtargets = []
for i in range(0, n_subgoals):
self.subtargets.append(self.path[i * step])
self.subtargets.append(self.path[-1])
self.next_subtarget = 0
print "The path produced " + str(len(self.subtargets)) + " subgoals"
self.counter_to_next_sub = self.count_limit
# Publish the path for visualization purposes
ros_path = Path()
ros_path.header.frame_id = "map"
for p in self.path:
ps = PoseStamped()
ps.header.frame_id = "map"
ps.pose.position.x = 0
ps.pose.position.y = 0
######################### NOTE: QUESTION ##############################
# Fill the ps.pose.position values to show the path in RViz
# You must understand what self.robot_perception.resolution
# and self.robot_perception.origin are.
# Multiply subgoal's coordinates with resolution and
# add robot.origin in order to translate between the (0,0) of the robot
# pose and (0,0) of the map
ps.pose.position.x = p[0] * self.robot_perception.resolution + \
self.robot_perception.origin['x']
ps.pose.position.y = p[1] * self.robot_perception.resolution + \
self.robot_perception.origin['y']
########################################################################
ros_path.poses.append(ps)
self.path_publisher.publish(ros_path)
# Publish the subtargets for visualization purposes
subtargets_mark = []
for s in self.subtargets:
subt = [
s[0] * self.robot_perception.resolution + \
self.robot_perception.origin['x'],
s[1] * self.robot_perception.resolution + \
self.robot_perception.origin['y']
]
subtargets_mark.append(subt)
RvizHandler.printMarker(\
subtargets_mark,\
2, # Type: Sphere
0, # Action: Add
"map", # Frame
"art_subtargets", # Namespace
[0, 0.8, 0.0, 0.8], # Color RGBA
0.2 # Scale
)
self.inner_target_exists = True
def velocitiesToNextSubtarget(self):
[linear, angular] = [0, 0]
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
theta = self.robot_perception.robot_pose['th']
######################### NOTE: QUESTION ##############################
# The velocities of the robot regarding the next subtarget should be
# computed. The known parameters are the robot pose [x,y,th] from
# robot_perception and the next_subtarget [x,y]. From these, you can
# compute the robot velocities for the vehicle to approach the target.
# Hint: Trigonometry is required
if self.subtargets and self.next_subtarget <= len(self.subtargets) - 1:
st_x = self.subtargets[self.next_subtarget][0]
st_y = self.subtargets[self.next_subtarget][1]
######################### NOTE: QUESTION ##############################
# angular and linear velocities are calculated upon the
# math types presented at 9.exploration_target_selection.pdf
dtheta = math.degrees(math.atan2(st_y - ry,
st_x - rx)) - math.degrees(theta)
if dtheta >= 0 and dtheta < 180:
angular = dtheta / 180
elif dtheta > 0 and dtheta >= 180:
angular = (dtheta - 2 * 180) / 180
elif dtheta <= 0 and dtheta > -180:
angular = dtheta / 180
elif dtheta < 0 and dtheta < -180:
angular = (dtheta + 2 * 180) / 180
#should be angular*0.3 but then the robot moves very slow
#and cannot reach the targets within the time set from the timer
if angular > 0.3:
angular = 0.3
elif angular < -0.3:
angular = -0.3
# **20 is used to avoid overshoot problem
linear = ((1 - abs(angular))**20) * 0.3
return [linear, angular]
class TargetSelection:
# Constructor
def __init__(self, selection_method):
self.goals_position = []
self.goals_value = []
self.omega = 0.0
self.radius = 0
self.method = selection_method
self.brush = Brushfires()
self.topo = Topology()
self.path_planning = PathPlanning()
def selectTarget(self, init_ogm, coverage, robot_pose, origin, \
resolution, force_random = False):
target = [-1, -1]
######################### NOTE: QUESTION ##############################
# Implement a smart way to select the next target. You have the
# following tools: ogm_limits, Brushfire field, OGM skeleton,
# topological nodes.
# Find only the useful boundaries of OGM. Only there calculations
# have meaning
ogm_limits = OgmOperations.findUsefulBoundaries(
init_ogm, origin, resolution)
# Blur the OGM to erase discontinuities due to laser rays
ogm = OgmOperations.blurUnoccupiedOgm(init_ogm, ogm_limits)
# Calculate Brushfire field
tinit = time.time()
brush = self.brush.obstaclesBrushfireCffi(ogm, ogm_limits)
Print.art_print("Brush time: " + str(time.time() - tinit),
Print.ORANGE)
# Calculate skeletonization
tinit = time.time()
skeleton = self.topo.skeletonizationCffi(ogm, \
origin, resolution, ogm_limits)
Print.art_print("Skeletonization time: " + str(time.time() - tinit),
Print.ORANGE)
# Find topological graph
tinit = time.time()
nodes = self.topo.topologicalNodes(ogm, skeleton, coverage, origin, \
resolution, brush, ogm_limits)
Print.art_print("Topo nodes time: " + str(time.time() - tinit),
Print.ORANGE)
# Visualization of topological nodes
vis_nodes = []
for n in nodes:
vis_nodes.append([
n[0] * resolution + origin['x'],
n[1] * resolution + origin['y']
])
RvizHandler.printMarker(\
vis_nodes,\
1, # Type: Arrow
0, # Action: Add
"map", # Frame
"art_topological_nodes", # Namespace
[0.3, 0.4, 0.7, 0.5], # Color RGBA
0.1 # Scale
)
# Check at autonomous_expl.yaml if target_selector = 'random' or 'smart'
if self.method == 'random' or force_random == True:
target = self.selectRandomTarget(ogm, coverage, brush, ogm_limits)
elif self.method == 'smart' and force_random == False:
tinit = time.time()
# Get the robot pose in pixels
[rx, ry] = [\
robot_pose['x_px'] - \
origin['x'] / resolution,\
robot_pose['y_px'] - \
origin['y'] / resolution\
]
g_robot_pose = [rx, ry]
# If nodes > 25 the calculation time-cost is very big
# In order to avoid time-reset we perform sampling on
# the nodes list and take a half-sized sample
for i in range(0, len(nodes)):
nodes[i].append(i)
if (len(nodes) > 25):
nodes = random.sample(nodes, int(len(nodes) / 2))
# Initialize weigths
w_dist = [0] * len(nodes)
w_rot = [robot_pose['th']] * len(nodes)
w_cov = [0] * len(nodes)
w_topo = [0] * len(nodes)
# Calculate weights for every node in the topological graph
for i in range(0, len(nodes)):
# If path planning fails then give extreme values to weights
path = self.path_planning.createPath(g_robot_pose, nodes[i],
resolution)
if not path:
w_dist[i] = sys.maxsize
w_rot[i] = sys.maxsize
w_cov[i] = sys.maxsize
w_topo[i] = sys.maxsize
else:
for j in range(1, len(path)):
# Distance cost
w_dist[i] += math.hypot(path[j][0] - path[j - 1][0],
path[j][1] - path[j - 1][1])
# Rotational cost
w_rot[i] += abs(
math.atan2(path[j][0] - path[j - 1][0],
path[j][1] - path[j - 1][1]))
# Coverage cost
w_cov[i] += coverage[int(path[j][0])][int(
path[j][1])] / (len(path))
# Add the coverage cost of 0-th node of the path
w_cov[i] += coverage[int(path[0][0])][int(
path[0][1])] / (len(path))
# Topological cost
# This metric depicts wether the target node
# is placed in open space or near an obstacle
# We want the metric to be reliable so we also check node's neighbour cells
w_topo[i] += brush[nodes[i][0]][nodes[i][1]]
w_topo[i] += brush[nodes[i][0] - 1][nodes[i][1]]
w_topo[i] += brush[nodes[i][0] + 1][nodes[i][1]]
w_topo[i] += brush[nodes[i][0]][nodes[i][-1]]
w_topo[i] += brush[nodes[i][0]][nodes[i][+1]]
w_topo[i] += brush[nodes[i][0] - 1][nodes[i][1] - 1]
w_topo[i] += brush[nodes[i][0] + 1][nodes[i][1] + 1]
w_topo[i] = w_topo[i] / 7
# Normalize weights between 0-1
for i in range(0, len(nodes)):
w_dist[i] = 1 - (w_dist[i] - min(w_dist)) / (max(w_dist) -
min(w_dist))
w_rot[i] = 1 - (w_rot[i] - min(w_rot)) / (max(w_rot) -
min(w_rot))
w_cov[i] = 1 - (w_cov[i] - min(w_cov)) / (max(w_cov) -
min(w_cov))
w_topo[i] = 1 - (w_topo[i] - min(w_topo)) / (max(w_topo) -
min(w_topo))
# Set cost values
# We set each cost's priority based on experimental results
# from "Cost-Based Target Selection Techniques Towards Full Space
# Exploration and Coverage for USAR Applications
# in a Priori Unknown Environments" publication
C1 = w_topo
C2 = w_dist
C3 = [1] * len(nodes)
for i in range(0, len(nodes)):
C3[i] -= w_cov[i]
C4 = w_rot
# Priority Weight
C_PW = [0] * len(nodes)
# Smoothing Factor
C_SF = [0] * len(nodes)
# Target's Final Priority
C_FP = [0] * len(nodes)
for i in range(0, len(nodes)):
C_PW[i] = 2**3 * (1 - C1[i]) / .5 + 2**2 * (
1 - C2[i]) / .5 + 2**1 * (1 - C3[i]) / .5 + 2**0 * (
1 - C4[i]) / .5
C_SF[i] = (2**3 * (1 - C1[i]) + 2**2 * (1 - C2[i]) + 2**1 *
(1 - C3[i]) + 2**0 * (1 - C4[i])) / (2**4 - 1)
C_FP[i] = C_PW[i] * C_SF[i]
# Select the node with the smallest C_FP value
val, idx = min((val, idx) for (idx, val) in enumerate(C_FP))
Print.art_print(
"Select smart target time: " + str(time.time() - tinit),
Print.BLUE)
Print.art_print("Selected Target - Node: " + str(nodes[idx][2]),
Print.BLUE)
target = nodes[idx]
else:
Print.art_print(
"[ERROR] target_selector at autonomous_expl.yaml must be either 'random' or 'smart'",
Print.RED)
########################################################################
return target
def selectRandomTarget(self, ogm, coverage, brushogm, ogm_limits):
# The next target in pixels
tinit = time.time()
next_target = [0, 0]
found = False
while not found:
x_rand = random.randint(0, ogm.shape[0] - 1)
y_rand = random.randint(0, ogm.shape[1] - 1)
if ogm[x_rand][y_rand] < 50 and coverage[x_rand][y_rand] < 50 and \
brushogm[x_rand][y_rand] > 5:
next_target = [x_rand, y_rand]
found = True
Print.art_print("Select random target time: " + str(time.time() - tinit), \
Print.ORANGE)
return next_target
class Navigation:
# Constructor
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.target_selection = TargetSelection()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.inner_target_exists = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# ROS Publisher for the path
self.path_publisher = rospy.Publisher(rospy.get_param('path_pub_topic'), \
Path, queue_size = 10)
# ROS Publisher for the subtargets
self.subtargets_publisher = rospy.Publisher(rospy.get_param('subgoals_pub_topic'),\
MarkerArray, queue_size = 10)
# ROS Publisher for the current target
self.current_target_publisher = rospy.Publisher(rospy.get_param('curr_target_pub_topic'),\
Marker, queue_size = 10)
def checkTarget(self, event):
# Check if we have a target or if the robot just wanders
if self.inner_target_exists == False or self.move_with_target == False or\
self.next_subtarget == len(self.subtargets):
return
# Get the robot pose in pixels
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
# YOUR CODE HERE ------------------------------------------------------
# Here the next subtarget is checked for proximity. If the robot is too
# close to the subtarget it is considered approached and the next
# subtarget is assigned as next. However there are cases where the
# robot misses one subtarget due to obstacle detection. Enhance the below
# functionality by checking all the subgoals (and the final goal) for
# proximity and assign the proper next subgoal
# Find the distance between the robot pose and the next subtarget
dist = math.hypot(\
rx - self.subtargets[self.next_subtarget][0], \
ry - self.subtargets[self.next_subtarget][1])
# Check if distance is less than 7 px (14 cm)
if dist < 7:
print "Sub target reached!"
self.next_subtarget += 1
# Check if the final subtarget has been approached
if self.next_subtarget == len(self.subtargets):
print "Final goal reached!"
self.target_exists = False
# ---------------------------------------------------------------------
# Publish the current target
if self.next_subtarget == len(self.subtargets):
return
st = Marker()
st.header.frame_id = "map"
st.ns = 'as_curr_namespace'
st.id = 0
st.header.stamp = rospy.Time(0)
st.type = 1 # arrow
st.action = 0 # add
st.pose.position.x = self.subtargets[self.next_subtarget][0]\
* self.robot_perception.resolution + \
self.robot_perception.origin['x']
st.pose.position.y = self.subtargets[self.next_subtarget][1]\
* self.robot_perception.resolution + \
self.robot_perception.origin['y']
st.color.r = 0
st.color.g = 0
st.color.b = 0.8
st.color.a = 0.8
st.scale.x = 0.2
st.scale.y = 0.2
st.scale.z = 0.2
self.current_target_publisher.publish(st)
# Function that seelects the next target, produces the path and updates
# the coverage field. This is called from the speeds assignment code, since
# it contains timer callbacks
def selectTarget(self):
# IMPORTANT: The robot must be stopped if you call this function until
# it is over
# Check if we have a map
while self.robot_perception.have_map == False:
return
print "Navigation: Producing new target"
# We are good to continue the exploration
# Make this true in order not to call it again from the speeds assignment
self.target_exists = True
# Manually update the coverage field
self.robot_perception.updateCoverage()
print "Navigation: Coverage updated"
# Gets copies of the map and coverage
local_ogm = self.robot_perception.getMap()
local_coverage = self.robot_perception.getCoverage()
print "Got the map and Coverage"
# Call the target selection function to select the next best goal
target = self.target_selection.selectTarget(\
local_ogm,\
local_coverage,\
self.robot_perception.robot_pose)
print "Navigation: New target: " + str(target)
# Once the target has been found, find the path to it
# Get the global robot pose
g_robot_pose = self.robot_perception.getGlobalCoordinates(\
[self.robot_perception.robot_pose['x_px'],\
self.robot_perception.robot_pose['y_px']])
# Need to reverse the path??
self.path = self.path_planning.createPath(\
local_ogm,\
g_robot_pose,\
target)
print "Navigation: Path for target found with " + str(len(self.path)) +\
" points"
# Reverse the path to start from the robot
self.path = self.path[::-1]
# Break the path to subgoals every 10 pixels (0.5m)
step = 10
n_subgoals = (int) (len(self.path)/step)
self.subtargets = []
for i in range(0, n_subgoals):
self.subtargets.append(self.path[i * step])
self.subtargets.append(self.path[-1])
self.next_subtarget = 0
print "The path produced " + str(len(self.subtargets)) + " subgoals"
# Publish the path for visualization purposes
ros_path = Path()
ros_path.header.frame_id = "map"
for p in self.path:
ps = PoseStamped()
ps.header.frame_id = "map"
ps.pose.position.x = p[0] * self.robot_perception.resolution + \
self.robot_perception.origin['x']
ps.pose.position.y = p[1] * self.robot_perception.resolution + \
self.robot_perception.origin['y']
ros_path.poses.append(ps)
self.path_publisher.publish(ros_path)
# Publish the subtargets for visualization purposes
ros_subgoals = MarkerArray()
count = 0
for s in self.subtargets:
st = Marker()
st.header.frame_id = "map"
st.ns = 'as_namespace'
st.id = count
st.header.stamp = rospy.Time(0)
st.type = 2 # sphere
st.action = 0 # add
st.pose.position.x = s[0] * self.robot_perception.resolution + \
self.robot_perception.origin['x']
st.pose.position.y = s[1] * self.robot_perception.resolution + \
self.robot_perception.origin['y']
st.color.r = 0
st.color.g = 0.8
st.color.b = 0
st.color.a = 0.8
st.scale.x = 0.2
st.scale.y = 0.2
st.scale.z = 0.2
ros_subgoals.markers.append(st)
count += 1
self.subtargets_publisher.publish(ros_subgoals)
self.inner_target_exists = True
def velocitiesToNextSubtarget(self):
[linear, angular] = [0, 0]
# YOUR CODE HERE ------------------------------------------------------
# The velocities of the robot regarding the next subtarget should be
# computed. The known parameters are the robot pose [x,y,th] from
# robot_perception and the next_subtarget [x,y]. From these, you can
# compute the robot velocities for the vehicle to approach the target.
# Hint: Trigonometry is required
# ---------------------------------------------------------------------
return [linear, angular]
import cv2
import numpy as np
from realsense.view import FieldView
from path_planning import PathPlanning
def f(x):
return x - 1250
planner = PathPlanning(send=False)
size = 720, 1280, 3
v = FieldView()
window_name = 'main'
cv2.namedWindow(window_name)
cv2.createTrackbar('table_1', window_name, f(1250), f(3750), int)
cv2.createTrackbar('table_2', window_name, f(1250), f(3750), int)
cv2.createTrackbar('table_3', window_name, f(1250), f(3750), int)
cv2.createTrackbar('zone', window_name, 0, 1, int)
cv2.setTrackbarPos('table_1', window_name, f(2500))
cv2.setTrackbarPos('table_2', window_name, f(2500))
cv2.setTrackbarPos('table_3', window_name, f(2500))
while True:
pos1 = cv2.getTrackbarPos('table_1', window_name) + 1250
pos2 = cv2.getTrackbarPos('table_2', window_name) + 1250
pos3 = cv2.getTrackbarPos('table_3', window_name) + 1250
zone = cv2.getTrackbarPos('zone', window_name)
move_table_pos = (pos1, pos2, pos3)
v.zone = zone
points, flip_points = planner.main((pos1, pos2, pos3, zone))
class Navigation:
# Constructor
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.select_another_target = 0
self.inner_target_exists = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
self.count_limit = 200 # 20 sec
self.counter_to_next_sub = self.count_limit
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# Read the target function
self.target_selector = rospy.get_param("target_selector")
print "The selected target function is " + self.target_selector
self.target_selection = TargetSelection(self.target_selector)
# ROS Publisher for the path
self.path_publisher = \
rospy.Publisher(rospy.get_param('path_pub_topic'), \
Path, queue_size = 10)
# ROS Publisher for the subtargets
self.subtargets_publisher = \
rospy.Publisher(rospy.get_param('subgoals_pub_topic'),\
MarkerArray, queue_size = 10)
# ROS Publisher for the current target
self.current_target_publisher = \
rospy.Publisher(rospy.get_param('curr_target_pub_topic'),\
Marker, queue_size = 10)
def checkTarget(self, event):
# Check if we have a target or if the robot just wanders
if self.inner_target_exists == False or self.move_with_target == False or\
self.next_subtarget == len(self.subtargets):
return
self.counter_to_next_sub -= 1
if self.counter_to_next_sub == 0:
Print.art_print('\n~~~~ Time reset ~~~~',Print.RED)
self.inner_target_exists = False
self.target_exists = False
return
# Get the robot pose in pixels
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
######################### NOTE: QUESTION ##############################
# What if a later subtarget or the end has been reached before the
# next subtarget? Alter the code accordingly.
# Challenge 5
# Instead of checking the distance of the next target,
# check the distance of the remaining targets, therefore you may reach to a next subject
# bypassing current.
for i in range(self.next_subtarget, len(self.subtargets)):
# Find the distance between the robot pose and the next subtarget
dist = math.hypot( rx - self.subtargets[i][0], ry - self.subtargets[i][1])
# check distance with the i_th target
if i != (len(self.subtargets)-1):
if dist < 10: #if distance found to be small from a terget set as next the target i + 1
self.next_subtarget = i + 1
self.counter_to_next_sub = self.count_limit
else:
if dist < 5: #if distance found to be small from a terget set as next the target i + 1
self.next_subtarget = i + 1
self.counter_to_next_sub = self.count_limit
# Check if the final subtarget has been approached
if self.next_subtarget == len(self.subtargets):
self.target_exists = False
########################################################################
# Publish the current target
if self.next_subtarget == len(self.subtargets):
return
subtarget = [\
self.subtargets[self.next_subtarget][0]\
* self.robot_perception.resolution + \
self.robot_perception.origin['x'],
self.subtargets[self.next_subtarget][1]\
* self.robot_perception.resolution + \
self.robot_perception.origin['y']\
]
RvizHandler.printMarker(\
[subtarget],\
1, # Type: Arrow
0, # Action: Add
"map", # Frame
"art_next_subtarget", # Namespace
[0, 0, 0.8, 0.8], # Color RGBA
0.2 # Scale
)
# Function that selects the next target, produces the path and updates
# the coverage field. This is called from the speeds assignment code, since
# it contains timer callbacks
def selectTarget(self):
# IMPORTANT: The robot must be stopped if you call this function until
# it is over
# Check if we have a map
while self.robot_perception.have_map == False:
Print.art_print("Navigation: No map yet", Print.RED)
return
print "\nClearing all markers"
RvizHandler.printMarker(\
[[0, 0]],\
1, # Type: Arrow
3, # Action: delete all
"map", # Frame
"null", # Namespace
[0,0,0,0], # Color RGBA
0.1 # Scale
)
print '\n\n----------------------------------------------------------'
print "Navigation: Producing new target"
# We are good to continue the exploration
# Make this true in order not to call it again from the speeds assignment
self.target_exists = True
# Gets copies of the map and coverage
local_ogm = self.robot_perception.getMap()
local_ros_ogm = self.robot_perception.getRosMap()
local_coverage = self.robot_perception.getCoverage()
print "Got the map and Coverage"
self.path_planning.setMap(local_ros_ogm)
# Once the target has been found, find the path to it
# Get the global robot pose
g_robot_pose = self.robot_perception.getGlobalCoordinates(\
[self.robot_perception.robot_pose['x_px'],\
self.robot_perception.robot_pose['y_px']])
# Call the target selection function to select the next best goal
# Choose target function
self.path = []
force_random = False
while len(self.path) == 0:
start = time.time()
target = self.target_selection.selectTarget(\
local_ogm,\
local_coverage,\
self.robot_perception.robot_pose,
self.robot_perception.origin,
self.robot_perception.resolution,
force_random)
self.path = self.path_planning.createPath(\
g_robot_pose,\
target,
self.robot_perception.resolution)
print "Navigation: Path for target found with " + str(len(self.path)) +\
" points"
if len(self.path) == 0:
Print.art_print(\
"Path planning failed. Fallback to random target selection", \
Print.RED)
force_random = True
# Reverse the path to start from the robot
self.path = self.path[::-1]
###################################################
# Extra Challenge 1
# Smooth path
if len(self.path) > 3:
x = np.array(self.path)
y = np.copy(x)
a = 0.5
b = 0.1
epsilon = np.sum(np.abs(a * (x[1:-1, :] - y[1:-1, :]) + b * (y[2:, :] - 2*y[1:-1, :] + y[:-2, :])))
while epsilon > 1e-3:
y[1:-1, :] += a * (x[1:-1, :] - y[1:-1, :]) + b * (y[2:, :] - 2*y[1:-1, :] + y[:-2, :])
epsilon = np.sum(np.abs(a * (x[1:-1, :] - y[1:-1, :]) + b * (y[2:, :] - 2*y[1:-1, :] + y[:-2, :])))
# Copy the smoother path
self.path = y.tolist()
####################################################
# Break the path to subgoals every 2 pixels (1m = 20px)
step = 1
n_subgoals = (int) (len(self.path)/step)
self.subtargets = []
for i in range(0, n_subgoals):
self.subtargets.append(self.path[i * step])
self.subtargets.append(self.path[-1])
self.next_subtarget = 0
print "The path produced " + str(len(self.subtargets)) + " subgoals"
######################### NOTE: QUESTION ##############################
# The path is produced by an A* algorithm. This means that it is
# optimal in length but 1) not smooth and 2) length optimality
# may not be desired for coverage-based exploration
########################################################################
self.counter_to_next_sub = self.count_limit
# Publish the path for visualization purposes
ros_path = Path()
ros_path.header.frame_id = "map"
for p in self.path:
ps = PoseStamped()
ps.header.frame_id = "map"
ps.pose.position.x = 0
ps.pose.position.y = 0
######################### NOTE: QUESTION ##############################
# Fill the ps.pose.position values to show the path in RViz
# You must understand what self.robot_perception.resolution
# and self.robot_perception.origin are.
# Challenge 2:
ps.pose.position.x = p[0] * self.robot_perception.resolution + self.robot_perception.origin['x']
ps.pose.position.y = p[1] * self.robot_perception.resolution + self.robot_perception.origin['y']
########################################################################
ros_path.poses.append(ps)
self.path_publisher.publish(ros_path)
# Publish the subtargets for visualization purposes
subtargets_mark = []
for s in self.subtargets:
subt = [
s[0] * self.robot_perception.resolution + \
self.robot_perception.origin['x'],
s[1] * self.robot_perception.resolution + \
self.robot_perception.origin['y']
]
subtargets_mark.append(subt)
RvizHandler.printMarker(\
subtargets_mark,\
2, # Type: Sphere
0, # Action: Add
"map", # Frame
"art_subtargets", # Namespace
[0, 0.8, 0.0, 0.8], # Color RGBA
0.2 # Scale
)
self.inner_target_exists = True
def velocitiesToNextSubtarget(self):
[linear, angular] = [0, 0]
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
theta = self.robot_perception.robot_pose['th']
######################### NOTE: QUESTION ##############################
# The velocities of the robot regarding the next subtarget should be
# computed. The known parameters are the robot pose [x,y,th] from
# robot_perception and the next_subtarget [x,y]. From these, you can
# compute the robot velocities for the vehicle to approach the target.
# Hint: Trigonometry is required
# Challenge 3
if self.subtargets and self.next_subtarget <= len(self.subtargets) - 1:
st_x = self.subtargets[self.next_subtarget][0]
st_y = self.subtargets[self.next_subtarget][1]
# We know goals position (x', y') and robot's position (x,y)
# It is tan(Theta_RG) = (y'-y)/(x'-x) = lamda
# Theta_RG = atan(lamda)
# Theta_RG is the angle between vector RobotGoal and Ox axis
Theta_RG = math.atan2(st_y - ry, st_x - rx) # atan2 return the result in rads
# We can calculate the angle between the direction of robot's movement
# and the RG vector. The angle will be the difference of RGangle - theta(direction angle of robot's movement)
D_Theta = Theta_RG - theta
# Based on presentation 9, D_Theta
# has to be readjusted in [-pi, pi]
if (D_Theta < math.pi) and (D_Theta > -math.pi):
omega = D_Theta / math.pi
elif D_Theta >= math.pi:
omega = (D_Theta - 2 * math.pi) / math.pi
elif D_Theta <= -math.pi:
omega = (D_Theta + 2 * math.pi) / math.pi
# Linear Speed Calculation
# presentation 9: u = umax(1- |omega|)^n
# larger n -> lower speed
# max speed = 0.3
u = ((1 - abs(omega))**(6.00))
linear = 0.3 * u
# Robot is steering slowly
# therefore we had to make steer faster
# max speed = 0.3
omega = (math.copysign(abs(omega)**(1.0/6), omega))
angular = 0.3 * omega
######################### NOTE: QUESTION ##############################
return [linear, angular]
class TargetSelection:
# Constructor
def __init__(self, selection_method):
self.goals_position = []
self.goals_value = []
self.omega = 0.0
self.radius = 0
self.method = selection_method
self.brush = Brushfires()
self.topo = Topology()
self.path_planning = PathPlanning()
# Initialize previous target
self.previous_target = [-1, -1]
def selectTarget(self, init_ogm, ros_ogm, coverage, robot_pose, origin, \
resolution, force_random = False):
target = [-1, -1]
######################### NOTE: QUESTION ##############################
# Implement a smart way to select the next target. You have the
# following tools: ogm_limits, Brushfire field, OGM skeleton,
# topological nodes.
# Find only the useful boundaries of OGM. Only there calculations
# have meaning
ogm_limits = OgmOperations.findUsefulBoundaries(
init_ogm, origin, resolution)
# Blur the OGM to erase discontinuities due to laser rays
ogm = OgmOperations.blurUnoccupiedOgm(init_ogm, ogm_limits)
# Calculate Brushfire field
tinit = time.time()
brush = self.brush.obstaclesBrushfireCffi(ogm, ogm_limits)
Print.art_print("Brush time: " + str(time.time() - tinit),
Print.ORANGE)
# Calculate skeletonization
tinit = time.time()
skeleton = self.topo.skeletonizationCffi(ogm, \
origin, resolution, ogm_limits)
Print.art_print("Skeletonization time: " + str(time.time() - tinit),
Print.ORANGE)
# Find topological graph
tinit = time.time()
nodes = self.topo.topologicalNodes(ogm, skeleton, coverage, origin, \
resolution, brush, ogm_limits)
Print.art_print("Topo nodes time: " + str(time.time() - tinit),
Print.ORANGE)
# Visualization of topological nodes
vis_nodes = []
for n in nodes:
vis_nodes.append([
n[0] * resolution + origin['x'],
n[1] * resolution + origin['y']
])
RvizHandler.printMarker(\
vis_nodes,\
1, # Type: Arrow
0, # Action: Add
"map", # Frame
"art_topological_nodes", # Namespace
[0.3, 0.4, 0.7, 0.5], # Color RGBA
0.1 # Scale
)
# Random point
if self.method == 'random' or force_random == True:
target = self.selectRandomTarget(ogm, coverage, brush, ogm_limits)
# Custom point
elif self.method == 'cost_based':
self.path_planning.setMap(ros_ogm)
g_robot_pose = [robot_pose['x_px'] - int(origin['x'] / resolution),\
robot_pose['y_px'] - int(origin['y'] / resolution)]
# Remove all covered nodes from the candidate list
nodes = np.array(nodes)
uncovered_idxs = np.array(
[coverage[n[0], n[1]] == 0 for n in nodes])
goals = nodes[uncovered_idxs]
# Initialize costs
w_dist = np.full(len(goals), np.inf)
w_turn = np.full(len(goals), np.inf)
w_topo = np.full(len(goals), np.inf)
w_cove = np.full(len(goals), np.inf)
for idx, node in zip(range(len(goals)), goals):
subgoals = np.array(
self.path_planning.createPath(g_robot_pose, node,
resolution))
# If path is empty then skip to the next iteration
if subgoals.size == 0:
continue
# subgoals should contain the robot pose, so we don't need to diff it explicitly
subgoal_vectors = np.diff(subgoals, axis=0)
# Distance cost
dists = [math.hypot(v[0], v[1]) for v in subgoal_vectors]
w_dist[idx] = np.sum(dists)
# Turning cost
w_turn[idx] = 0
theta = robot_pose['th']
for v in subgoal_vectors[:-1]:
st_x, st_y = v
theta2 = math.atan2(st_y - g_robot_pose[1],
st_x - g_robot_pose[0])
w_turn[idx] += abs(theta2 - theta)
theta = theta2
# Coverage cost
w_cove[idx] = sum(coverage[x][y] for x, y in subgoal_vectors)
# Topology cost
w_topo[idx] = brush[node[0], node[1]]
# Normalize weights
w_dist = (w_dist - min(w_dist)) / (max(w_dist) - min(w_dist))
w_turn = (w_turn - min(w_turn)) / (max(w_turn) - min(w_turn))
w_cove = (w_cove - min(w_cove)) / (max(w_cove) - min(w_cove))
w_topo = (w_topo - min(w_topo)) / (max(w_topo) - min(w_topo))
# Cost weights
c_topo = 4
c_dist = 3
c_cove = 2
c_turn = 1
# Calculate combination cost (final)
cost = c_dist * w_dist + c_turn * w_turn + c_cove * w_cove + c_topo * w_topo
min_dist, min_idx = min(zip(cost, range(len(cost))))
target = nodes[min_idx]
# Check if next target exists and if it exists, check if is close to previous
if target is None:
target = self.selectRandomTarget(ogm, coverage, brush,
ogm_limits)
else:
target_dist = math.hypot(target[0] - self.previous_target[0],
target[1] - self.previous_target[1])
if target_dist <= 5:
target = self.selectRandomTarget(ogm, coverage, brush,
ogm_limits)
########################################################################
self.previous_target = target
return target
def selectRandomTarget(self, ogm, coverage, brushogm, ogm_limits):
# The next target in pixels
tinit = time.time()
next_target = [0, 0]
found = False
while not found:
x_rand = random.randint(0, ogm.shape[0] - 1)
y_rand = random.randint(0, ogm.shape[1] - 1)
if ogm[x_rand][y_rand] < 50 and coverage[x_rand][y_rand] < 50 and \
brushogm[x_rand][y_rand] > 5:
next_target = [x_rand, y_rand]
found = True
Print.art_print("Select random target time: " + str(time.time() - tinit), \
Print.ORANGE)
return next_target
class Navigation:
# Constructor
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.select_another_target = 0
self.inner_target_exists = False
self.TimeOut = False
self.Reset = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
self.count_limit = 230 # 23 sec
self.counter_to_next_sub = self.count_limit
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.1), self.checkTarget)
# Read the target function
self.target_selector = rospy.get_param("target_selector")
print "The selected target function is " + self.target_selector
self.target_selection = TargetSelection(self.target_selector)
# ROS Publisher for the path
self.path_publisher = \
rospy.Publisher(rospy.get_param('path_pub_topic'), \
Path, queue_size = 10)
# ROS Publisher for the subtargets
self.subtargets_publisher = \
rospy.Publisher(rospy.get_param('subgoals_pub_topic'),\
MarkerArray, queue_size = 10)
# ROS Publisher for the current target
self.current_target_publisher = \
rospy.Publisher(rospy.get_param('curr_target_pub_topic'),\
Marker, queue_size = 10)
def checkTarget(self, event):
# Check if we have a target or if the robot just wanders
if self.inner_target_exists == False or self.move_with_target == False or\
self.next_subtarget == len(self.subtargets):
return
self.counter_to_next_sub -= 1
if self.counter_to_next_sub == 0:
Print.art_print('\n~~~~ Time reset ~~~~',Print.RED)
self.inner_target_exists = False
self.target_exists = False
if self.Reset == False:
self.TimeOut = True
else:
self.Reset = False
return
# Get the robot pose in pixels
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
# remove visited sub targets
self.subtargets = self.subtargets[self.next_subtarget:]
# set the next sub target as the first one that should be visited
self.next_subtarget = 0
dis_x = [rx - target[0] for target in self.subtargets]
dis_y = [ry - target[1] for target in self.subtargets]
#for i in range(0,len(dis_x)):
# print " x dis " + str(dis_x[i])
# print " y dis " + str(dis_y[i])
# target distances
dist_t = [math.hypot(dis[0],dis[1]) for dis in zip(dis_x,dis_y) ]
# find the closest target
min_target = 0
min_dis = dist_t[0]
for i in range(0,len(dist_t)):
if min_dis > dist_t[i]:
min_dis = dist_t[i]
min_target = i
######################### NOTE: QUESTION ##############################
# What if a later subtarget or the end has been reached before the
# next subtarget? Alter the code accordingly.
# print " min target " + str(min_target) + " min dis " + str(min_dis)
# Check if distance is less than 7 px (14 cm)
if min_dis < 5:
self.next_subtarget = min_target + 1
self.counter_to_next_sub = self.count_limit
# print "REACHED"
# print " next target " + str(self.next_subtarget) + " len " +str(len(self.subtargets))
# Check if the final subtarget has been approached
if self.next_subtarget == len(self.subtargets):
self.target_exists = False
########################################################################
# Publish the current target
if self.next_subtarget == len(self.subtargets):
return
subtarget = [\
self.subtargets[self.next_subtarget][0]\
* self.robot_perception.resolution + \
self.robot_perception.origin['x'],
self.subtargets[self.next_subtarget][1]\
* self.robot_perception.resolution + \
self.robot_perception.origin['y']\
]
RvizHandler.printMarker(\
[subtarget],\
1, # Type: Arrow
0, # Action: Add
"map", # Frame
"art_next_subtarget", # Namespace
[0, 0, 0.8, 0.8], # Color RGBA
0.2 # Scale
)
# Function that selects the next target, produces the path and updates
# the coverage field. This is called from the speeds assignment code, since
# it contains timer callbacks
def selectTarget(self):
# IMPORTANT: The robot must be stopped if you call this function until
# it is over
# Check if we have a map
while self.robot_perception.have_map == False:
Print.art_print("Navigation: No map yet", Print.RED)
return
print "\nClearing all markers"
RvizHandler.printMarker(\
[[0, 0]],\
1, # Type: Arrow
3, # Action: delete all
"map", # Frame
"null", # Namespace
[0,0,0,0], # Color RGBA
0.1 # Scale
)
print '\n\n----------------------------------------------------------'
print "Navigation: Producing new target"
# We are good to continue the exploration
# Make this true in order not to call it again from the speeds assignment
self.target_exists = True
# Gets copies of the map and coverage
local_ogm = self.robot_perception.getMap()
local_ros_ogm = self.robot_perception.getRosMap()
local_coverage = self.robot_perception.getCoverage()
print "Got the map and Coverage"
self.path_planning.setMap(local_ros_ogm)
# Once the target has been found, find the path to it
# Get the global robot pose
g_robot_pose = self.robot_perception.getGlobalCoordinates(\
[self.robot_perception.robot_pose['x_px'],\
self.robot_perception.robot_pose['y_px']])
# Call the target selection function to select the next best goal
# Choose target function
print "TimeOut Flag " +str(self.TimeOut)
self.path = []
force_random = False
while len(self.path) == 0:
start = time.time()
target = self.target_selection.selectTarget(\
local_ogm,\
local_coverage,\
self.robot_perception.robot_pose,
self.robot_perception.origin,
self.robot_perception.resolution,
force_random)
self.path = self.path_planning.createPath(\
g_robot_pose,\
target,
self.robot_perception.resolution)
print "Navigation: Path for target found with " + str(len(self.path)) +\
" points"
if len(self.path) == 0:
Print.art_print(\
"Path planning failed. Fallback to random target selection", \
Print.RED)
force_random = True
# Reverse the path to start from the robot
self.path = self.path[::-1]
tolerance = 0.000001
weight_data = 0.5
weight_smooth = 0.1
new = deepcopy(self.path)
dims = 2
change = tolerance
# Path smoothing using gradient descent
if len(self.path) > 3:
print "PATH SMOOTHING"
while change >= tolerance:
change = 0.0
for i in range(1, len(new) - 1):
for j in range(dims):
x_i = self.path[i][j]
y_i, y_prev, y_next = new[i][j], new[i - 1][j], new[i + 1][j]
y_i_saved = y_i
y_i += weight_data * (x_i - y_i) + weight_smooth * (y_next + y_prev - (2 * y_i))
new[i][j] = y_i
change += abs(y_i - y_i_saved)
self.path = new
# Break the path to subgoals every 2 pixels (1m = 20px)
step = 1
n_subgoals = (int) (len(self.path)/step)
self.subtargets = []
for i in range(0, n_subgoals):
self.subtargets.append(self.path[i * step])
self.subtargets.append(self.path[-1])
self.next_subtarget = 0
print "The path produced " + str(len(self.subtargets)) + " subgoals"
######################### NOTE: QUESTION ##############################
# The path is produced by an A* algorithm. This means that it is
# optimal in length but 1) not smooth and 2) length optimality
# may not be desired for coverage-based exploration
########################################################################
self.counter_to_next_sub = self.count_limit
# Publish the path for visualization purposes
ros_path = Path()
ros_path.header.frame_id = "map"
for p in self.path:
ps = PoseStamped()
ps.header.frame_id = "map"
######################### NOTE: QUESTION ##############################
# Fill the ps.pose.position values to show the path in RViz
# You must understand what self.robot_perception.resolution
# and self.robot_perception.origin are.
#p[0] * resolution => gives the path's point x-coordinate expressed in the map's coordinate system (meters)
# As the path's(p) first point-set is the pixels that are occupied by the robot, multyplying it by resolution (meter/pixel)
# gives the values of that point expressed in meters.
# Adding the robot's initial position we have a path starting from the robot's position expressed in the robot's frame.
ps.pose.position.x = p[0]*0.05 + self.robot_perception.origin['x']
ps.pose.position.y = p[1]*0.05 + self.robot_perception.origin['y']
# print " x val " + str(ps.pose.position.x)
# print " y val " + str(ps.pose.position.y )
# print " origin x " + str(self.robot_perception.origin['x'])
# print " origin y " + str(self.robot_perception.origin['y'])
# print " p0 " +str(p[0])
# print " p1 " +str(p[1])
########################################################################
ros_path.poses.append(ps)
self.path_publisher.publish(ros_path)
# Publish the subtargets for visualization purposes
subtargets_mark = []
for s in self.subtargets:
subt = [
s[0] * self.robot_perception.resolution + \
self.robot_perception.origin['x'],
s[1] * self.robot_perception.resolution + \
self.robot_perception.origin['y']
]
subtargets_mark.append(subt)
RvizHandler.printMarker(\
subtargets_mark,\
2, # Type: Sphere
0, # Action: Add
"map", # Frame
"art_subtargets", # Namespace
[0, 0.8, 0.0, 0.8], # Color RGBA
0.2 # Scale
)
self.inner_target_exists = True
def velocitiesToNextSubtarget(self):
[linear, angular] = [0, 0]
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
theta = self.robot_perception.robot_pose['th']
######################### NOTE: QUESTION ##############################
# The velocities of the robot regarding the next subtarget should be
# computed. The known parameters are the robot pose [x,y,th] from
# robot_perception and the next_subtarget [x,y]. From these, you can
# compute the robot velocities for the vehicle to approach the target.
# Hint: Trigonometry is required
# if there is another subtarget , acquire its position
if self.subtargets and self.next_subtarget <= len(self.subtargets) - 1:
st_x = self.subtargets[self.next_subtarget][0]
st_y = self.subtargets[self.next_subtarget][1]
# align robot with the target
theta_target = np.arctan2(st_y - ry, st_x - rx)
delta_theta = (theta_target - theta)
# find omega
if delta_theta > np.pi:
omega = (delta_theta - 2*np.pi)/np.pi
elif delta_theta < -np.pi:
omega = (delta_theta + 2*np.pi)/np.pi
else:
omega = delta_theta/np.pi
# maximum magnitude for both velocities is 0.3
linear = 0.3 * ((1 - np.abs(omega)) ** 5)
angular = 0.3 * np.sign(omega)
######################### NOTE: QUESTION ##############################
return [linear, angular]
class Navigation:
# Constructor
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.select_another_target = 0
self.inner_target_exists = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
self.count_limit = 200 # 20 sec
self.counter_to_next_sub = self.count_limit
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# Read the target function
self.target_selector = rospy.get_param("target_selector")
print "The selected target function is " + self.target_selector
self.target_selection = TargetSelection(self.target_selector)
# ROS Publisher for the path
self.path_publisher = \
rospy.Publisher(rospy.get_param('path_pub_topic'), \
Path, queue_size = 10)
# ROS Publisher for the subtargets
self.subtargets_publisher = \
rospy.Publisher(rospy.get_param('subgoals_pub_topic'),\
MarkerArray, queue_size = 10)
# ROS Publisher for the current target
self.current_target_publisher = \
rospy.Publisher(rospy.get_param('curr_target_pub_topic'),\
Marker, queue_size = 10)
def checkTarget(self, event):
# Check if we have a target or if the robot just wanders
if self.inner_target_exists == False or self.move_with_target == False or\
self.next_subtarget == len(self.subtargets):
return
self.counter_to_next_sub -= 1
if self.counter_to_next_sub == 0:
Print.art_print('\n~~~~ Time reset ~~~~',Print.RED)
self.inner_target_exists = False
self.target_exists = False
return
# Get the robot pose in pixels
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
# Find the distance between the robot pose and the next subtarget
dist = math.hypot(\
rx - self.subtargets[self.next_subtarget][0], \
ry - self.subtargets[self.next_subtarget][1])
######################### NOTE: QUESTION ##############################
# What if a later subtarget or the end has been reached before the
# next subtarget? Alter the code accordingly.
# Check if distance is less than 7 px (14 cm)
if dist < 5:
self.next_subtarget += 1
self.counter_to_next_sub = self.count_limit
# Check if the final subtarget has been approached
if self.next_subtarget == len(self.subtargets):
self.target_exists = False
else:
# Find the next subtarget with the minimum distance
min = dist # minimum distance from a next subtarget
jump_to_next_subtarget = self.next_subtarget # the number of the next subtarget to jump to
next_check = self.next_subtarget + 1 # next subtarget to be checked
for i in range(self.next_subtarget+1, len(self.subtargets)):
temp_dist = math.hypot(\
rx - self.subtargets[next_check][0], \
ry - self.subtargets[next_check][1])
if temp_dist < min:
min = temp_dist
jump_to_next_subtarget = i
next_check += 1
self.next_subtarget = jump_to_next_subtarget
self.counter_to_next_sub = self.count_limit
########################################################################
# Publish the current target
if self.next_subtarget == len(self.subtargets):
return
subtarget = [\
self.subtargets[self.next_subtarget][0]\
* self.robot_perception.resolution + \
self.robot_perception.origin['x'],
self.subtargets[self.next_subtarget][1]\
* self.robot_perception.resolution + \
self.robot_perception.origin['y']\
]
RvizHandler.printMarker(\
[subtarget],\
1, # Type: Arrow
0, # Action: Add
"map", # Frame
"art_next_subtarget", # Namespace
[0, 0, 0.8, 0.8], # Color RGBA
0.2 # Scale
)
# Function that selects the next target, produces the path and updates
# the coverage field. This is called from the speeds assignment code, since
# it contains timer callbacks
def selectTarget(self):
# IMPORTANT: The robot must be stopped if you call this function until
# it is over
# Check if we have a map
while self.robot_perception.have_map == False:
Print.art_print("Navigation: No map yet", Print.RED)
return
print "\nClearing all markers"
RvizHandler.printMarker(\
[[0, 0]],\
1, # Type: Arrow
3, # Action: delete all
"map", # Frame
"null", # Namespace
[0,0,0,0], # Color RGBA
0.1 # Scale
)
print '\n\n----------------------------------------------------------'
print "Navigation: Producing new target"
# We are good to continue the exploration
# Make this true in order not to call it again from the speeds assignment
self.target_exists = True
# Gets copies of the map and coverage
local_ogm = self.robot_perception.getMap()
local_ros_ogm = self.robot_perception.getRosMap()
local_coverage = self.robot_perception.getCoverage()
print "Got the map and Coverage"
self.path_planning.setMap(local_ros_ogm)
# Once the target has been found, find the path to it
# Get the global robot pose
g_robot_pose = self.robot_perception.getGlobalCoordinates(\
[self.robot_perception.robot_pose['x_px'],\
self.robot_perception.robot_pose['y_px']])
# Call the target selection function to select the next best goal
# Choose target function
self.path = []
force_random = False
while len(self.path) == 0:
start = time.time()
target = self.target_selection.selectTarget(\
local_ogm,\
local_coverage,\
self.robot_perception.robot_pose,
self.robot_perception.origin,
self.robot_perception.resolution,
force_random)
self.path = self.path_planning.createPath(\
g_robot_pose,\
target,
self.robot_perception.resolution)
print "Navigation: Path for target found with " + str(len(self.path)) +\
" points"
if len(self.path) == 0:
Print.art_print(\
"Path planning failed. Fallback to random target selection", \
Print.RED)
force_random = True
# Reverse the path to start from the robot
self.path = self.path[::-1]
#########################################
# Extra challenge #1
# A. Smooth path planner
if len(self.path) > 3:
x = np.array(self.path)
y = np.copy(x)
a = 0.5
b = 0.1
# FORMULA : y_i = y_i + a * (x_i - y_i) + b * (y_i+1 - 2 * y_i + y_i+1)
epsilon = np.sum(np.abs(a * (x[1:-1, :] - y[1:-1, :]) + b * (y[2:, :] - 2*y[1:-1, :] + y[:-2, :])))
while epsilon > 1e-3:
y[1:-1, :] += a * (x[1:-1, :] - y[1:-1, :]) + b * (y[2:, :] - 2*y[1:-1, :] + y[:-2, :])
epsilon = np.sum(np.abs(a * (x[1:-1, :] - y[1:-1, :]) + b * (y[2:, :] - 2*y[1:-1, :] + y[:-2, :])))
# Copy the smoother path
self.path = y.tolist()
# Break the path to subgoals every 2 pixels (1m = 20px)
step = 1
n_subgoals = (int) (len(self.path)/step)
self.subtargets = []
for i in range(0, n_subgoals):
self.subtargets.append(self.path[i * step])
self.subtargets.append(self.path[-1])
self.next_subtarget = 0
print "The path produced " + str(len(self.subtargets)) + " subgoals"
######################### NOTE: QUESTION ##############################
# The path is produced by an A* algorithm. This means that it is
# optimal in length but 1) not smooth and 2) length optimality
# may not be desired for coverage-based exploration
########################################################################
self.counter_to_next_sub = self.count_limit
# Publish the path for visualization purposes
ros_path = Path()
ros_path.header.frame_id = "map"
for p in self.path:
ps = PoseStamped()
ps.header.frame_id = "map"
ps.pose.position.x = 0
ps.pose.position.y = 0
######################### NOTE: QUESTION ##############################
# Fill the ps.pose.position values to show the path in RViz
# You must understand what self.robot_perception.resolution
# and self.robot_perception.origin are.
ps.pose.position.x = p[0] * self.robot_perception.resolution + self.robot_perception.origin['x']
ps.pose.position.y = p[1] * self.robot_perception.resolution + self.robot_perception.origin['y']
########################################################################
ros_path.poses.append(ps)
self.path_publisher.publish(ros_path)
# Publish the subtargets for visualization purposes
subtargets_mark = []
for s in self.subtargets:
subt = [
s[0] * self.robot_perception.resolution + \
self.robot_perception.origin['x'],
s[1] * self.robot_perception.resolution + \
self.robot_perception.origin['y']
]
subtargets_mark.append(subt)
RvizHandler.printMarker(\
subtargets_mark,\
2, # Type: Sphere
0, # Action: Add
"map", # Frame
"art_subtargets", # Namespace
[0, 0.8, 0.0, 0.8], # Color RGBA
0.2 # Scale
)
self.inner_target_exists = True
def velocitiesToNextSubtarget(self):
[linear, angular] = [0, 0]
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
theta = self.robot_perception.robot_pose['th'] # robot orientation
######################### NOTE: QUESTION ##############################
# The velocities of the robot regarding the next subtarget should be
# computed. The known parameters are the robot pose [x,y,th] from
# robot_perception and the next_subtarget [x,y]. From these, you can
# compute the robot velocities for the vehicle to approach the target.
# Hint: Trigonometry is required
if self.subtargets and self.next_subtarget <= len(self.subtargets) - 1:
st_x = self.subtargets[self.next_subtarget][0]
st_y = self.subtargets[self.next_subtarget][1]
PI = 3.14
MAX_SPEED = 0.3
st_x_rel = st_x - rx
st_y_rel = st_y - ry
st_th = math.atan2(st_y_rel, st_x_rel)
delta = st_th - theta
if delta >= 0 and delta < PI:
angular = delta / PI
elif delta > 0 and delta >= PI:
angular = (delta - 2 * PI) / PI
elif delta <= 0 and delta > -PI:
angular = delta / PI
elif delta < 0 and delta < -PI:
angular = (delta + 2 * PI) / PI
angular *= 5 #add more twisting sensitivity
angular *= MAX_SPEED
if angular > 0.3:
angular = 0.3
elif angular < -0.3:
angular = -0.3
linear = ((1 - abs(angular)) ** 10 ) * MAX_SPEED
######################### NOTE: QUESTION ##############################
return [linear, angular]
class Navigation:
# Constructor
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.select_another_target = 0
self.inner_target_exists = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
self.count_limit = 200 # 20 sec
self.counter_to_next_sub = self.count_limit
self.laser_aggregation = LaserDataAggregator()
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# Read the target function
self.target_selector = rospy.get_param("target_selector")
print "The selected target function is" + self.target_selector
self.target_selection = TargetSelection(self.target_selector)
# ROS Publisher for the path
self.path_publisher = \
rospy.Publisher(rospy.get_param('path_pub_topic'), \
Path, queue_size = 10)
# ROS Publisher for the subtargets
self.subtargets_publisher = \
rospy.Publisher(rospy.get_param('subgoals_pub_topic'),\
MarkerArray, queue_size = 10)
# ROS Publisher for the current target
self.current_target_publisher = \
rospy.Publisher(rospy.get_param('curr_target_pub_topic'),\
Marker, queue_size = 10)
def checkTarget(self, event):
# Check if we have a target or if the robot just wanders
if self.inner_target_exists == False or self.move_with_target == False or\
self.next_subtarget == len(self.subtargets):
return
self.counter_to_next_sub -= 1
if self.counter_to_next_sub == 0:
Print.art_print('\n~~~~ Time reset ~~~~', Print.RED)
self.inner_target_exists = False
self.target_exists = False
return
# Get the robot pose in pixels
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
# Find the distance between the robot pose and the next subtarget
dist = math.hypot(\
rx - self.subtargets[self.next_subtarget][0], \
ry - self.subtargets[self.next_subtarget][1])
######################### NOTE: QUESTION ##############################
# What if a later subtarget or the end has been reached before the
# next subtarget? Alter the code accordingly.
# Check if distance is less than 7 px (14 cm)
count = 1
for i in range(
self.next_subtarget,
len(self.subtargets) - 2
): # compare the slope of the points till the end in order to check if there are more than one points at the same line
try: # use try except to avoid zerodivisior error
slope1 = (self.subtargets[i + 1][1] -
self.subtargets[i][1]) / (self.subtargets[i + 1][0] -
self.subtargets[i][0])
except ZeroDivisionError:
slope1 = float('Inf')
try:
slope2 = (self.subtargets[i + 2][1] - self.subtargets[i + 1][1]
) / (self.subtargets[i + 2][0] -
self.subtargets[i + 1][0])
except ZeroDivisionError:
slope2 = float('Inf')
if abs(slope1 - slope2) < 0.3 or (
slope1 == float('inf') and slope2 == float('inf')
): # if difference in slopes is less than 0.3 then we assume that the points are in the same line
count += 1
else:
break
scan = self.laser_aggregation.laser_scan # take scan measurements
count_scan = 0
for i in range(
250, 450
): # count scanlines with measure more than 2. The range has calculated after experiments.
if scan[i] > 2:
count_scan += 1
if dist < 5:
if count != 1:
self.next_subtarget += count # if there are more than one subtargets that belongs to the same line then the robot goes to the last target of the line
self.counter_to_next_sub = self.count_limit + count * 100 # increase time limit proportionally to the count in order to avoid time reset
elif count_scan > 10: # check if obstacle closer than 4 cm exists, if obstacle does not exist then
self.next_subtarget += 2 # increase subtarget by 2. This case happens when count = 1 in order to accelerate the process in case of not having subtargets in the same line
self.counter_to_next_sub = self.count_limit + 300 # increase time limit in order to avoid time reset
if self.next_subtarget >= len(
self.subtargets
): # check if the final subtarget has been approached
self.next_subtarget = len(self.subtargets)
else:
self.next_subtarget += 1 # else if obstacle exists then increase subtarget by 1
self.counter_to_next_sub = self.count_limit + 300 # increase time limit in order to avoid time reset
if self.next_subtarget >= len(
self.subtargets
): # check if the final subtarget has been approached
self.next_subtarget = len(self.subtargets)
if self.next_subtarget == len(self.subtargets):
self.target_exists = False
# Publish the current target
if self.next_subtarget == len(self.subtargets):
return
subtarget = [\
self.subtargets[self.next_subtarget][0]\
* self.robot_perception.resolution + \
self.robot_perception.origin['x'],
self.subtargets[self.next_subtarget][1]\
* self.robot_perception.resolution + \
self.robot_perception.origin['y']\
]
RvizHandler.printMarker(\
[subtarget],\
1, # Type: Arrow
0, # Action: Add
"map", # Frame
"art_next_subtarget", # Namespace
[0, 0, 0.8, 0.8], # Color RGBA
0.2 # Scale
)
# Function that selects the next target, produces the path and updates
# the coverage field. This is called from the speeds assignment code, since
# it contains timer callbacks
def selectTarget(self):
# IMPORTANT: The robot must be stopped if you call this function until
# it is over
# Check if we have a map
while self.robot_perception.have_map == False:
Print.art_print("Navigation: No map yet", Print.RED)
return
print "\nClearing all markers"
RvizHandler.printMarker(\
[[0, 0]],\
1, # Type: Arrow
3, # Action: delete all
"map", # Frame
"null", # Namespace
[0,0,0,0], # Color RGBA
0.1 # Scale
)
print '\n\n----------------------------------------------------------'
print "Navigation: Producing new target"
# We are good to continue the exploration
# Make this true in order not to call it again from the speeds assignment
self.target_exists = True
# Gets copies of the map and coverage
local_ogm = self.robot_perception.getMap()
local_ros_ogm = self.robot_perception.getRosMap()
local_coverage = self.robot_perception.getCoverage()
print "Got the map and Coverage"
self.path_planning.setMap(local_ros_ogm)
# Once the target has been found, find the path to it
# Get the global robot pose
g_robot_pose = self.robot_perception.getGlobalCoordinates(\
[self.robot_perception.robot_pose['x_px'],\
self.robot_perception.robot_pose['y_px']])
# Call the target selection function to select the next best goal
# Choose target function
self.path = []
force_random = False
while len(self.path) == 0:
start = time.time()
target = self.target_selection.selectTarget(\
local_ogm,\
local_coverage,\
self.robot_perception.robot_pose,
self.robot_perception.origin,
self.robot_perception.resolution,
force_random)
self.path = self.path_planning.createPath(\
g_robot_pose,\
target,
self.robot_perception.resolution)
print "Navigation: Path for target found with " + str(len(self.path)) +\
" points"
if len(self.path) == 0:
Print.art_print(\
"Path planning failed. Fallback to random target selection", \
Print.RED)
force_random = True
# Reverse the path to start from the robot
self.path = self.path[::-1]
# Break the path to subgoals every 2 pixels (1m = 20px)
step = 1
n_subgoals = (int)(len(self.path) / step)
self.subtargets = []
for i in range(0, n_subgoals):
self.subtargets.append(self.path[i * step])
self.subtargets.append(self.path[-1])
self.next_subtarget = 0
print "The path produced " + str(len(self.subtargets)) + " subgoals"
######################### NOTE: QUESTION ##############################
# The path is produced by an A* algorithm. This means that it is
# optimal in length but 1) not smooth and 2) length optimality
# may not be desired for coverage-based exploration
########################################################################
self.counter_to_next_sub = self.count_limit
# Publish the path for visualization purposes
ros_path = Path()
ros_path.header.frame_id = "map"
for p in self.path:
ps = PoseStamped()
ps.header.frame_id = "map"
ps.pose.position.x = 0
ps.pose.position.y = 0
######################### NOTE: QUESTION ##############################
# Fill the ps.pose.position values to show the path in RViz
# You must understand what self.robot_perception.resolution
# and self.robot_perception.origin are.
# p[0] and p[1] are the global coordinates so we multiply these values with resolution and add the origin
ps.pose.position.x = p[
0] * self.robot_perception.resolution + self.robot_perception.origin[
'x']
ps.pose.position.y = p[
1] * self.robot_perception.resolution + self.robot_perception.origin[
'y']
########################################################################
ros_path.poses.append(ps)
self.path_publisher.publish(ros_path)
# Publish the subtargets for visualization purposes
subtargets_mark = []
for s in self.subtargets:
subt = [
s[0] * self.robot_perception.resolution + \
self.robot_perception.origin['x'],
s[1] * self.robot_perception.resolution + \
self.robot_perception.origin['y']
]
subtargets_mark.append(subt)
RvizHandler.printMarker(\
subtargets_mark,\
2, # Type: Sphere
0, # Action: Add
"map", # Frame
"art_subtargets", # Namespace
[0, 0.8, 0.0, 0.8], # Color RGBA
0.2 # Scale
)
self.inner_target_exists = True
def velocitiesToNextSubtarget(self):
[linear, angular] = [0, 0]
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
theta = self.robot_perception.robot_pose['th']
######################### NOTE: QUESTION ##############################
# The velocities of the robot regarding the next subtarget should be
# computed. The known parameters are the robot pose [x,y,th] from
# robot_perception and the next_subtarget [x,y]. From these, you can
# compute the robot velocities for the vehicle to approach the target.
# Hint: Trigonometry is required
if self.subtargets and self.next_subtarget <= len(self.subtargets) - 1:
st_x = self.subtargets[self.next_subtarget][0]
st_y = self.subtargets[self.next_subtarget][1]
######################### NOTE: QUESTION ##############################
# theta contains yaw angle, hence we convert it to x,y coordinates in order to find the angle in degrees
x = math.cos(theta)
y = math.sin(theta)
deg = math.degrees(math.atan2(y, x))
delta_theta = math.degrees(math.atan2(st_y - ry, st_x - rx)) - deg
if delta_theta >= 0 and delta_theta < 180:
angular = delta_theta / 180
elif delta_theta > 0 and delta_theta >= 180:
angular = (delta_theta - 2 * 180) / 180
elif delta_theta <= 0 and delta_theta > -180:
angular = delta_theta / 180
elif delta_theta < 0 and delta_theta < -180:
angular = (delta_theta + 2 * 180) / 180
linear = (
(1 - abs(angular))**25
) # power calculated after experiments and we use it in order to avoid overshoot problem
linear = linear * 0.3 # use tanh to limit the range
if angular > 0.3: # angular = angular * 0.3. angular velocity should be multiplied by max angular velocity according to notes
angular = 0.3
elif angular < -0.3:
angular = -0.3
# but the result was much slower so we didn't multiply it by 0.3
######################### NOTE: QUESTION ##############################
return [linear, angular]
class Navigation:
# Constructor
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.select_another_target = 0
self.inner_target_exists = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
self.count_limit = 200 # 20 sec
self.counter_to_next_sub = self.count_limit
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# Read the target function
self.target_selector = rospy.get_param("target_selector")
print("The selected target function is " + self.target_selector)
self.target_selection = TargetSelection(self.target_selector)
# ROS Publisher for the path
self.path_publisher = \
rospy.Publisher(rospy.get_param('path_pub_topic'),
Path, queue_size=10)
# ROS Publisher for the subtargets
self.subtargets_publisher = \
rospy.Publisher(rospy.get_param('subgoals_pub_topic'),
MarkerArray, queue_size=10)
# ROS Publisher for the current target
self.current_target_publisher = \
rospy.Publisher(rospy.get_param('curr_target_pub_topic'),
Marker, queue_size=10)
def checkTarget(self, event):
# Check if we have a target or if the robot just wanders
if self.inner_target_exists == False or self.move_with_target == False or \
self.next_subtarget == len(self.subtargets):
return
self.counter_to_next_sub -= 1
if self.counter_to_next_sub == 0:
Print.art_print('\n~~~~ Time reset ~~~~', Print.RED)
self.inner_target_exists = False
self.target_exists = False
return
# Get the robot pose in pixels
[rx, ry] = [
self.robot_perception.robot_pose['x_px'] -
self.robot_perception.origin['x'] /
self.robot_perception.resolution,
self.robot_perception.robot_pose['y_px'] -
self.robot_perception.origin['y'] /
self.robot_perception.resolution
]
# # Find the distance between the robot pose and the next subtarget
# dist = math.hypot(
# rx - self.subtargets[self.next_subtarget][0],
# ry - self.subtargets[self.next_subtarget][1])
######################### NOTE: QUESTION ##############################
# What if a later subtarget or the end has been reached before the
# next subtarget? Alter the code accordingly.
# Check if distance is less than 7 px (14 cm)
# if dist < 5:
# self.next_subtarget += 1
# self.counter_to_next_sub = self.count_limit
# # Check if the final subtarget has been approached
# if self.next_subtarget == len(self.subtargets):
# self.target_exists = False
# find the distance between robot and ALL subtargets
dists = (math.hypot(rx - self.subtargets[subtarget][0],
ry - self.subtargets[subtarget][1])
for subtarget, _ in enumerate(self.subtargets))
# check the distance of each subtarget from the robot
# go towards the nearest and change index of next_subtarget
# accordingly
for idx, dist in enumerate(dists):
if dist < 5:
self.next_subtarget = idx + 1
self.counter_to_next_sub = self.count_limit
# Check if the final subtarget has been approached
if self.next_subtarget == len(self.subtargets):
self.target_exists = False
########################################################################
# Publish the current target
if self.next_subtarget == len(self.subtargets):
return
subtarget = [
self.subtargets[self.next_subtarget][0] *
self.robot_perception.resolution +
self.robot_perception.origin['x'],
self.subtargets[self.next_subtarget][1] *
self.robot_perception.resolution +
self.robot_perception.origin['y']
]
RvizHandler.printMarker(
[subtarget],
1, # Type: Arrow
0, # Action: Add
"map", # Frame
"art_next_subtarget", # Namespace
[0, 0, 0.8, 0.8], # Color RGBA
0.2 # Scale
)
# Function that selects the next target, produces the path and updates
# the coverage field. This is called from the speeds assignment code, since
# it contains timer callbacks
def selectTarget(self):
# IMPORTANT: The robot must be stopped if you call this function until
# it is over
# Check if we have a map
while not self.robot_perception.have_map:
Print.art_print("Navigation: No map yet", Print.RED)
return
print("\nClearing all markers")
RvizHandler.printMarker( \
[[0, 0]], \
1, # Type: Arrow
3, # Action: delete all
"map", # Frame
"null", # Namespace
[0, 0, 0, 0], # Color RGBA
0.1 # Scale
)
print('\n\n----------------------------------------------------------')
print("Navigation: Producing new target")
# We are good to continue the exploration
# Make this true in order not to call it again from the speeds assignment
self.target_exists = True
# Gets copies of the map and coverage
local_ogm = self.robot_perception.getMap()
local_ros_ogm = self.robot_perception.getRosMap()
local_coverage = self.robot_perception.getCoverage()
print("Got the map and Coverage")
self.path_planning.setMap(local_ros_ogm)
# Once the target has been found, find the path to it
# Get the global robot pose
g_robot_pose = self.robot_perception.getGlobalCoordinates([
self.robot_perception.robot_pose['x_px'],
self.robot_perception.robot_pose['y_px']
])
# Call the target selection function to select the next best goal
# Choose target function
self.path = []
force_random = False
while len(self.path) == 0:
start = time.time()
print(self.robot_perception.resolution)
target = self.target_selection.selectTarget(
local_ogm, local_coverage, self.robot_perception.robot_pose,
self.robot_perception.origin, self.robot_perception.resolution,
force_random)
self.path = self.path_planning.createPath(
g_robot_pose, target, self.robot_perception.resolution)
print("Navigation: Path for target found with " + str(len(self.path)) + \
" points")
if len(self.path) == 0:
Print.art_print(
"Path planning failed. Fallback to random target selection",
Print.RED)
force_random = True
# Reverse the path to start from the robot
self.path = self.path[::-1]
# Break the path to subgoals every 2 pixels (1m = 20px)
step = 1
n_subgoals = (int)(len(self.path) / step)
self.subtargets = []
for i in range(0, n_subgoals):
self.subtargets.append(self.path[i * step])
self.subtargets.append(self.path[-1])
self.next_subtarget = 0
print("The path produced " + str(len(self.subtargets)) + " subgoals")
######################### NOTE: QUESTION ##############################
# The path is produced by an A* algorithm. This means that it is
# optimal in length but 1) not smooth and 2) length optimality
# may not be desired for coverage-based exploration
########################################################################
self.counter_to_next_sub = self.count_limit
# Publish the path for visualization purposes
ros_path = Path()
ros_path.header.frame_id = "map"
for p in self.path:
ps = PoseStamped()
ps.header.frame_id = "map"
ps.pose.position.x = 0
ps.pose.position.y = 0
######################### NOTE: QUESTION ##############################
# Fill the ps.pose.position values to show the path in RViz
# You must understand what self.robot_perception.resolution
# and self.robot_perception.origin are.
ps.pose.position.x = p[
0] * self.robot_perception.resolution + self.robot_perception.origin[
'x']
ps.pose.position.y = p[
1] * self.robot_perception.resolution + self.robot_perception.origin[
'y']
########################################################################
ros_path.poses.append(ps)
self.path_publisher.publish(ros_path)
# Publish the subtargets for visualization purposes
subtargets_mark = []
for s in self.subtargets:
subt = [
s[0] * self.robot_perception.resolution + \
self.robot_perception.origin['x'],
s[1] * self.robot_perception.resolution + \
self.robot_perception.origin['y']
]
subtargets_mark.append(subt)
RvizHandler.printMarker(
subtargets_mark,
2, # Type: Sphere
0, # Action: Add
"map", # Frame
"art_subtargets", # Namespace
[0, 0.8, 0.0, 0.8], # Color RGBA
0.2 # Scale
)
self.inner_target_exists = True
def velocitiesToNextSubtarget(self):
[linear, angular] = [0, 0]
[rx, ry] = [
self.robot_perception.robot_pose['x_px'] -
self.robot_perception.origin['x'] /
self.robot_perception.resolution,
self.robot_perception.robot_pose['y_px'] -
self.robot_perception.origin['y'] /
self.robot_perception.resolution
]
theta = self.robot_perception.robot_pose['th']
######################### NOTE: QUESTION ##############################
# The velocities of the robot regarding the next subtarget should be
# computed. The known parameters are the robot pose [x,y,th] from
# robot_perception and the next_subtarget [x,y]. From these, you can
# compute the robot velocities for the vehicle to approach the target.
# Hint: Trigonometry is required
if self.subtargets and self.next_subtarget <= len(self.subtargets) - 1:
st_x = self.subtargets[self.next_subtarget][0]
st_y = self.subtargets[self.next_subtarget][1]
# Find the distance between the robot and the next subtarget
# dist = math.hypot(rx - st_x, ry - st_y)
# phi is the angle between the robot and the next subtarget
# we calculate that angle using atan2, which returns the angle
# between a point [x,y] and the x'x axis, with origin on 0,0
# So, we move the origin to the robots position by subtracting
# its coordinates from a given point
phi = math.atan2(st_y - ry, st_x - rx)
if 3.2 >= (
phi - theta
) >= 0.1 and phi > theta: # if phi is bigger than theta and their difference is
# lower than pi (they are anti-parallel), turn left
linear = 0
angular = 1
elif 3.2 >= (
theta - phi
) >= 0.1 and phi < theta: # if phi is smaller than theta and their difference is
# lower than pi (they are anti-parallel), turn right
linear = 0
angular = -1
else: # otherwise, move ahead
linear = 1
angular = 0
######################### NOTE: QUESTION #############-#################
return [linear, angular]
class Navigation:
# Constructor
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.select_another_target = 0
self.inner_target_exists = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
self.count_limit = 500 # 20 sec
self.counter_to_next_sub = self.count_limit
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# Read the target function
self.target_selector = rospy.get_param("target_selector")
print "The selected target function is " + self.target_selector
self.target_selection = TargetSelection(self.target_selector)
# ROS Publisher for the path
self.path_publisher = rospy.Publisher(
rospy.get_param('path_pub_topic'), Path, queue_size=10)
# ROS Publisher for the subtargets
self.subtargets_publisher = rospy.Publisher(
rospy.get_param('subgoals_pub_topic'), MarkerArray, queue_size=10)
# ROS Publisher for the current target
self.current_target_publisher = rospy.Publisher(
rospy.get_param('curr_target_pub_topic'), Marker, queue_size=10)
self.previous_next_subtarget = None
def checkTarget(self, event):
"""
Check if target/sub-target reached.
:param event:
:return:
"""
if self.previous_next_subtarget is None or self.previous_next_subtarget < self.next_subtarget:
Print.art_print("next_subtarget = %d" % self.next_subtarget,
Print.ORANGE)
self.previous_next_subtarget = self.next_subtarget
# Check if we have a target or if the robot just wanders
if self.inner_target_exists == False \
or self.move_with_target == False \
or self.next_subtarget == len(self.subtargets):
return
self.counter_to_next_sub -= 1
if self.counter_to_next_sub == 0:
Print.art_print('\n~~~~ Time reset ~~~~', Print.RED)
self.inner_target_exists = False
self.target_exists = False
return
# Get the robot pose in: pixel units & global (map's) coordinate system
rp_l_px = [
self.robot_perception.robot_pose['x_px'],
self.robot_perception.robot_pose['y_px']
]
# rp_g_px = self.robot_perception.toGlobalCoordinates(rp_l_px)
[rx, ry] = [
self.robot_perception.robot_pose['x_px'] -
self.robot_perception.origin['x'] /
self.robot_perception.resolution,
self.robot_perception.robot_pose['y_px'] -
self.robot_perception.origin['y'] /
self.robot_perception.resolution
]
rp_g_px = [rx, ry]
# Find the distance between the robot pose and the next subtarget
v_g_px = map(operator.sub, rp_g_px,
self.subtargets[self.next_subtarget])
dist = math.hypot(v_g_px[0], v_g_px[1])
######################### NOTE: QUESTION ##############################
# What if a later subtarget or the end has been reached before the
# next subtarget? Alter the code accordingly.
# Check if distance is less than 7 px (14 cm)
# if dist < 5:
# self.next_subtarget += 1
# self.counter_to_next_sub = self.count_limit
# # Check if the final subtarget has been approached
# if self.next_subtarget == len(self.subtargets):
# self.target_exists = False
# Instead of checking only next sub-target check all remaining sub-targets (starting from end up to next
# sub-target) and decide if robot is closer to another sub-target at later point in path
for st_i in range(
len(self.subtargets) - 1, self.next_subtarget - 1, -1):
# @v_st_g_px: Difference of Global coordinates in Pixels between robot's position
# and i-th" sub-target's position
v_st_g_px = map(operator.sub, rp_g_px, self.subtargets[st_i])
if math.hypot(v_st_g_px[0], v_st_g_px[1]) < 5:
# reached @st_i, set next sub-target in path as robot's next sub-target
self.next_subtarget = st_i + 1
self.counter_to_next_sub = self.count_limit
if self.next_subtarget == len(self.subtargets):
self.target_exists = False
########################################################################
# Publish the current target
if self.next_subtarget == len(self.subtargets):
return
subtarget = [
self.subtargets[self.next_subtarget][0] *
self.robot_perception.resolution +
self.robot_perception.origin['x'],
self.subtargets[self.next_subtarget][1] *
self.robot_perception.resolution +
self.robot_perception.origin['y']
]
RvizHandler.printMarker(
[subtarget],
1, # Type: Arrow
0, # Action: Add
"map", # Frame
"art_next_subtarget", # Namespace
[0, 0, 0.8, 0.8], # Color RGBA
0.2 # Scale
)
# Function that selects the next target, produces the path and updates
# the coverage field. This is called from the speeds assignment code, since
# it contains timer callbacks
def selectTarget(self):
# IMPORTANT: The robot must be stopped if you call this function until
# it is over
# Check if we have a map
while not self.robot_perception.have_map:
Print.art_print("Navigation: No map yet", Print.RED)
return
print "\nClearing all markers"
RvizHandler.printMarker(
[[0, 0]],
1, # Type: Arrow
3, # Action: delete all
"map", # Frame
"null", # Namespace
[0, 0, 0, 0], # Color RGBA
0.1 # Scale
)
print '\n\n----------------------------------------------------------'
print "Navigation: Producing new target"
# We are good to continue the exploration
# Make this true in order not to call it again from the speeds assignment
self.target_exists = True
# Gets copies of the map and coverage
local_ogm = self.robot_perception.getMap()
local_ros_ogm = self.robot_perception.getRosMap()
local_coverage = self.robot_perception.getCoverage()
print "Got the map and Coverage"
self.path_planning.setMap(local_ros_ogm)
# Once the target has been found, find the path to it
# Get the global robot pose
# Get robot pose in global (map's) coordinates
rp_l_px = [
self.robot_perception.robot_pose['x_px'],
self.robot_perception.robot_pose['y_px']
]
rp_g_px = self.robot_perception.toGlobalCoordinates(
rp_l_px, with_resolution=True)
g_robot_pose = rp_g_px
# Call the target selection function to select the next best goal
# Choose target function
self.path = []
force_random = False
while len(self.path) == 0:
start = time.time()
target = self.target_selection.selectTarget(
local_ogm, local_coverage, self.robot_perception.robot_pose,
self.robot_perception.origin, self.robot_perception.resolution,
force_random)
self.path = self.path_planning.createPath(
g_robot_pose, target, self.robot_perception.resolution)
print "Navigation: Path for target found with " + str(len(self.path)) + \
" points"
if len(self.path) == 0:
Print.art_print(
"Path planning failed. Fallback to random target selection",
Print.RED)
force_random = True
# Reverse the path to start from the robot
self.path = self.path[::-1]
# Break the path to sub-goals every 2 pixels (1m = 20px)
step = 1
n_subgoals = int(len(self.path) / step)
self.subtargets = []
for i in range(0, n_subgoals):
self.subtargets.append(self.path[i * step])
self.subtargets.append(self.path[-1])
self.next_subtarget = 0
print "The path produced " + str(len(self.subtargets)) + " subgoals"
######################### NOTE: QUESTION ##############################
# The path is produced by an A* algorithm. This means that it is
# optimal in length but 1) not smooth and 2) length optimality
# may not be desired for coverage-based exploration
########################################################################
self.counter_to_next_sub = self.count_limit
# Publish the path for visualization purposes
ros_path = Path()
ros_path.header.frame_id = "map"
for p in self.path:
ps = PoseStamped()
ps.header.frame_id = "map"
######################### NOTE: QUESTION ##############################
# Fill the ps.pose.position values to show the path in RViz
# You must understand what self.robot_perception.resolution
# and self.robot_perception.origin are.
# Convert p from pixel-units to meter
p_m = self.robot_perception.toMeterUnits(p, False)
# Convert p_m from global (map's) coordinates to relative (image's) coordinates (using resolution = 1)
p_m_r = self.robot_perception.toRelativeCoordinates(p_m, False)
# Fill in $ps object
ps.pose.position.x = p_m_r[0]
ps.pose.position.y = p_m_r[1]
########################################################################
ros_path.poses.append(ps)
self.path_publisher.publish(ros_path)
# Publish the subtargets for visualization purposes
subtargets_mark = []
for s in self.subtargets:
st_m = self.robot_perception.toMeterUnits(s, False)
st_m_r = self.robot_perception.toRelativeCoordinates(st_m, False)
subtargets_mark.append(st_m_r)
RvizHandler.printMarker(
subtargets_mark,
2, # Type: Sphere
0, # Action: Add
"map", # Frame
"art_subtargets", # Namespace
[0, 0.8, 0.0, 0.8], # Color RGBA
0.2 # Scale
)
self.inner_target_exists = True
def velocitiesToNextSubtarget(self):
[linear, angular] = [0, 0]
# Get global robot coordinates
rp_l_px = [
self.robot_perception.robot_pose['x_px'],
self.robot_perception.robot_pose['y_px']
]
rp_g_px = self.robot_perception.toGlobalCoordinates(rp_l_px)
theta_r = self.robot_perception.robot_pose['th']
theta_r_deg = math.degrees(theta_r)
if theta_r_deg < 0:
theta_r_deg += 360
######################### NOTE: QUESTION ##############################
# The velocities of the robot regarding the next subtarget should be
# computed. The known parameters are the robot pose [x,y,th] from
# robot_perception and the next_subtarget [x,y]. From these, you can
# compute the robot velocities for the vehicle to approach the target.
# Hint: Trigonometry is required
if self.subtargets and self.next_subtarget <= len(self.subtargets) - 1:
# st: in pixel units & on global (map's) coordinate system
st_g_px = self.subtargets[self.next_subtarget]
# Both, st and rp are in the same coordinate system (global - map's) and in the same units (pixels)
# So, the vector difference of these two vectors (st - rp) yields the vector that joins these two points
v_g_px = map(operator.sub, st_g_px, rp_g_px)
# Robot's current orientations forms $theta_r angle from global coordinate system's x-axis, whereas the
# vector calculated above forms $theta_rg angle. The latter is defined next:
theta_rg = math.atan2(v_g_px[1], v_g_px[0])
# FIX: use degrees to correctly address negative angles
theta_rg_deg = math.degrees(theta_rg)
if theta_rg_deg < 0:
theta_rg_deg += 360
# Find angle difference, $delta_theta
delta_theta_deg = theta_rg_deg - theta_r_deg
# Calculate new angular velocity based on delta_theta
# (slide #93 - 9th presentation, IRS Course ECE AUTH, [email protected])
if 0 <= delta_theta_deg < 180:
angular = delta_theta_deg / 180
elif delta_theta_deg > 0 and delta_theta_deg >= 180:
angular = (delta_theta_deg - 360) / 180
elif -180 < delta_theta_deg <= 0:
angular = delta_theta_deg / 180
elif delta_theta_deg < 0 and delta_theta_deg < -180:
angular = (delta_theta_deg + 360) / 180
# Calculate new linear velocity based on new angular velocity and relation in slide #97 (for n = 6)
linear = math.pow(1 - abs(angular), 6)
######################### NOTE: QUESTION ##############################
return [linear, angular]
class Navigation(object):
MAX_LINEAR_VELOCITY = rospy.get_param('max_linear_velocity')
MAX_ANGULAR_VELOCITY = rospy.get_param('max_angular_velocity')
# Constructor
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.select_another_target = 0
self.inner_target_exists = False
self.force_random = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
self.count_limit = 200 # 20 sec
self.counter_to_next_sub = self.count_limit
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# Read the target function
self.target_selector = rospy.get_param("target_selector")
print "The selected target function is " + self.target_selector
self.target_selection = TargetSelection(self.target_selector)
# ROS Publisher for the path
self.path_publisher = rospy.Publisher(
rospy.get_param('path_pub_topic'), Path, queue_size=10)
# ROS Publisher for the subtargets
self.subtargets_publisher = rospy.Publisher(
rospy.get_param('subgoals_pub_topic'), MarkerArray, queue_size=10)
# ROS Publisher for the current target
self.current_target_publisher = rospy.Publisher(
rospy.get_param('curr_target_pub_topic'), Marker, queue_size=10)
def checkTarget(self, event):
# Check if we have a target or if the robot just wanders
if not self.inner_target_exists or not self.move_with_target or self.next_subtarget == len(
self.subtargets):
return
self.counter_to_next_sub -= 1
if self.counter_to_next_sub == 0:
Print.art_print('\n~~~~ Time reset ~~~~', Print.RED)
self.inner_target_exists = False
self.target_exists = False
self.force_random = True
return
# Get the robot pose in pixels
[rx, ry] = [
self.robot_perception.robot_pose['x_px'] -
self.robot_perception.origin['x'] /
self.robot_perception.resolution,
self.robot_perception.robot_pose['y_px'] -
self.robot_perception.origin['y'] /
self.robot_perception.resolution
]
######################### NOTE: QUESTION ##############################
# What if a later subtarget or the end has been reached before the next subtarget? Alter the code
# accordingly. Check if distance is less than 7 px (14 cm). Slice the list of subtargets and then reverse it.
for idx, st in enumerate(self.subtargets[self.next_subtarget::][::-1]):
# Right now, idx refers to the sliced & reversed array, fix it.
idx = len(self.subtargets) - 1 - idx
assert idx >= self.next_subtarget
dist = math.hypot(rx - st[0], ry - st[1])
if dist < 7:
self.next_subtarget = idx + 1
self.counter_to_next_sub = self.count_limit
if self.next_subtarget == len(self.subtargets):
self.target_exists = False
break
########################################################################
# Publish the current target
if self.next_subtarget == len(self.subtargets):
return
subtarget = [
self.subtargets[self.next_subtarget][0] *
self.robot_perception.resolution +
self.robot_perception.origin['x'],
self.subtargets[self.next_subtarget][1] *
self.robot_perception.resolution +
self.robot_perception.origin['y']
]
RvizHandler.printMarker(
[subtarget],
1, # Type: Arrow
0, # Action: Add
"map", # Frame
"art_next_subtarget", # Namespace
[0, 0, 0.8, 0.8], # Color RGBA
0.2 # Scale
)
# Function that selects the next target, produces the path and updates
# the coverage field. This is called from the speeds assignment code, since
# it contains timer callbacks
def selectTarget(self):
# IMPORTANT: The robot must be stopped if you call this function until
# it is over
# Check if we have a map
while not self.robot_perception.have_map:
Print.art_print("Navigation: No map yet", Print.RED)
return
print "\nClearing all markers"
RvizHandler.printMarker(
[[0, 0]],
1, # Type: Arrow
3, # Action: delete all
"map", # Frame
"null", # Namespace
[0, 0, 0, 0], # Color RGBA
0.1 # Scale
)
print '\n\n----------------------------------------------------------'
print "Navigation: Producing new target"
# We are good to continue the exploration
# Make this true in order not to call it again from the speeds assignment
self.target_exists = True
# Gets copies of the map and coverage
local_ogm = self.robot_perception.getMap()
local_ros_ogm = self.robot_perception.getRosMap()
local_coverage = self.robot_perception.getCoverage()
print "Got the map and Coverage"
self.path_planning.setMap(local_ros_ogm)
# Once the target has been found, find the path to it
# Get the global robot pose
g_robot_pose = self.robot_perception.getGlobalCoordinates([
self.robot_perception.robot_pose['x_px'],
self.robot_perception.robot_pose['y_px']
])
# Call the target selection function to select the next best goal
# Choose target function
self.path = []
while len(self.path) == 0:
start = time.time()
target = self.target_selection.selectTarget(
local_ogm, local_coverage, self.robot_perception.robot_pose,
self.robot_perception.origin, self.robot_perception.resolution,
self.force_random)
self.force_random = False
self.path = self.path_planning.createPath(
g_robot_pose, target, self.robot_perception.resolution)
print "Navigation: Path for target found with " + str(
len(self.path)) + " points"
if len(self.path) == 0:
Print.art_print(
"Path planning failed. Fallback to random target selection",
Print.RED)
self.force_random = True
# Reverse the path to start from the robot
self.path = self.path[::-1]
# Break the path to subgoals every 2 pixels (1m = 20px)
step = 1
n_subgoals = int(len(self.path) / step)
self.subtargets = []
for i in range(0, n_subgoals):
self.subtargets.append(self.path[i * step])
self.subtargets.append(self.path[-1])
self.next_subtarget = 0
print "The path produced " + str(len(self.subtargets)) + " subgoals"
######################### NOTE: QUESTION ##############################
# The path is produced by an A* algorithm. This means that it is
# optimal in length but 1) not smooth and 2) length optimality
# may not be desired for coverage-based exploration
########################################################################
self.counter_to_next_sub = self.count_limit
# Publish the path for visualization purposes
ros_path = Path()
ros_path.header.frame_id = "map"
for p in self.path:
ps = PoseStamped()
ps.header.frame_id = "map"
######################### NOTE: QUESTION ##############################
# Fill the ps.pose.position values to show the path in RViz
# You must understand what self.robot_perception.resolution
# and self.robot_perception.origin are.
ps.pose.position.x = p[
0] * self.robot_perception.resolution + self.robot_perception.origin[
'x']
ps.pose.position.y = p[
1] * self.robot_perception.resolution + self.robot_perception.origin[
'y']
########################################################################
ros_path.poses.append(ps)
self.path_publisher.publish(ros_path)
# Publish the subtargets for visualization purposes
subtargets_mark = []
for s in self.subtargets:
subt = [
s[0] * self.robot_perception.resolution +
self.robot_perception.origin['x'],
s[1] * self.robot_perception.resolution +
self.robot_perception.origin['y']
]
subtargets_mark.append(subt)
RvizHandler.printMarker(
subtargets_mark,
2, # Type: Sphere
0, # Action: Add
"map", # Frame
"art_subtargets", # Namespace
[0, 0.8, 0.0, 0.8], # Color RGBA
0.2 # Scale
)
self.inner_target_exists = True
def velocitiesToNextSubtarget(self):
rx = self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution
ry = self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution
theta = self.robot_perception.robot_pose['th']
######################### NOTE: QUESTION ##############################
# The velocities of the robot regarding the next subtarget should be
# computed. The known parameters are the robot pose [x,y,th] from
# robot_perception and the next_subtarget [x,y]. From these, you can
# compute the robot velocities for the vehicle to approach the target.
# Hint: Trigonometry is required
if self.subtargets and self.next_subtarget <= len(self.subtargets) - 1:
st_x = self.subtargets[self.next_subtarget][0]
st_y = self.subtargets[self.next_subtarget][1]
theta_g = math.atan2(st_y - ry, st_x - rx)
delta_theta = theta_g - theta # All thetas in radians.
angular_coeff = delta_theta / math.pi + 2 * (
delta_theta < -math.pi) - 2 * (delta_theta >= math.pi)
linear_coeff = (1 - abs(angular_coeff))**6
# Experimental: Recalculate angular speed according to linear.
angular_coeff = (1 - linear_coeff) * np.sign(angular_coeff)
linear = self.MAX_LINEAR_VELOCITY * linear_coeff
angular = self.MAX_ANGULAR_VELOCITY * angular_coeff
assert abs(angular_coeff
) <= 1, "Angular coefficient outside of range [-1, 1]."
assert 0 <= linear_coeff <= 1, "Linear coefficient outside of range [0, 1]."
return linear, angular
else:
return 0, 0
class TargetSelection(object):
# Constructor
def __init__(self, selection_method):
self.goals_position = []
self.goals_value = []
self.omega = 0.0
self.radius = 0
self.method = selection_method
if self.method_is_cost_based():
from robot_perception import RobotPerception
self.robot_perception = RobotPerception()
self.cost_based_properties = rospy.get_param("cost_based_properties")
numpy.set_printoptions(precision=3, threshold=numpy.nan, suppress=True)
self.brush = Brushfires()
self.path_planning = PathPlanning()
def method_is_cost_based(self):
return self.method in ['cost_based', 'cost_based_fallback']
def selectTarget(self, ogm, coverage, robot_pose, origin, resolution, force_random=False):
######################### NOTE: QUESTION ##############################
# Implement a smart way to select the next target. You have the following tools: ogm_limits, Brushfire field,
# OGM skeleton, topological nodes.
# Find only the useful boundaries of OGM. Only there calculations have meaning.
ogm_limits = OgmOperations.findUsefulBoundaries(ogm, origin, resolution)
# Blur the OGM to erase discontinuities due to laser rays
ogm = OgmOperations.blurUnoccupiedOgm(ogm, ogm_limits)
# Calculate Brushfire field
tinit = time.time()
brush = self.brush.obstaclesBrushfireCffi(ogm, ogm_limits)
Print.art_print("Brush time: " + str(time.time() - tinit), Print.ORANGE)
# Calculate skeletonization
tinit = time.time()
skeleton = topology.skeletonizationCffi(ogm, origin, resolution, ogm_limits)
Print.art_print("Skeletonization time: " + str(time.time() - tinit), Print.ORANGE)
# Find topological graph
tinit = time.time()
nodes = topology.topologicalNodes(ogm, skeleton, coverage, brush)
Print.art_print("Topo nodes time: " + str(time.time() - tinit), Print.ORANGE)
# Visualization of topological nodes
vis_nodes = [[n[0] * resolution + origin['x'], n[1] * resolution + origin['y']] for n in nodes]
RvizHandler.printMarker(
vis_nodes,
1, # Type: Arrow
0, # Action: Add
"map", # Frame
"art_topological_nodes", # Namespace
[0.3, 0.4, 0.7, 0.5], # Color RGBA
0.1 # Scale
)
try:
tinit = time.time()
except:
tinit = None
if self.method == 'random' or force_random:
target = self.selectRandomTarget(ogm, coverage, brush)
elif self.method_is_cost_based():
g_robot_pose = self.robot_perception.getGlobalCoordinates([robot_pose['x_px'], robot_pose['y_px']])
self.path_planning.setMap(self.robot_perception.getRosMap())
class MapContainer:
def __init__(self):
self.skeleton = skeleton
self.coverage = coverage
self.ogm = ogm
self.brush = brush
self.nodes = [node for node in nodes if TargetSelection.is_good(brush, ogm, coverage, node)]
self.robot_px = [robot_pose['x_px'] - origin['x'] / resolution,
robot_pose['y_px'] - origin['y'] / resolution]
self.theta = robot_pose['th']
@staticmethod
def create_path(path_target):
return self.path_planning.createPath(g_robot_pose, path_target, resolution)
target = self.select_by_cost(MapContainer())
else:
assert False, "Invalid target_selector method."
if tinit is not None:
Print.art_print("Target method {} time: {}".format(self.method, time.time() - tinit), Print.ORANGE)
return target
@staticmethod
def selectRandomTarget(ogm, coverage, brushogm):
# The next target in pixels
while True:
x_rand = random.randint(0, ogm.shape[0] - 1)
y_rand = random.randint(0, ogm.shape[1] - 1)
if ogm[x_rand][y_rand] < 50 and coverage[x_rand][y_rand] < 50 and brushogm[x_rand][y_rand] > 5:
return x_rand, y_rand
@staticmethod
def is_good(brush, ogm, coverage, target=None):
"""
:rtype: numpy.ndarray
"""
if target is not None:
x, y = target
return ogm[x][y] < 50 and coverage[x][y] < 50 and brush[x][y] > 5
else:
return numpy.logical_and(numpy.logical_and(ogm < 50, coverage < 50), brush > 5)
def select_by_cost(self, map_info):
nodes, paths, topo_costs = zip(*self.choose_best_nodes(map_info))
if nodes[0] is None: # choose_best_node's yield when no path is found will make nodes = (None,)
return -1, -1
elif len(nodes) == 1:
return nodes[0]
best_path_idx = self.weight_costs(
topo_costs,
[self.distance_cost(path, map_info.robot_px) for path in paths], # Distance
[self.coverage_cost(path, map_info.coverage) for path in paths], # Coverage
[self.rotation_cost(path, map_info.robot_px, map_info.theta) for path in paths] # Rotational
).argmax()
assert paths[best_path_idx]
target = nodes[best_path_idx]
Print.art_print("Best: {}".format(best_path_idx), Print.BLUE)
return target
def choose_best_nodes(self, map_info):
# Since path planning takes a lot of time for many nodes we should reduce the possible result to the nodes
# with the best distance and topological costs.
if self.method == 'cost_based_fallback':
yield self.closer_node(map_info), None, None
return
nodes = list(self.cluster_nodes(map_info.nodes, map_info.robot_px, self.cost_based_properties['node_clusters']))
topo_costs = [self._topological_cost(node, map_info.ogm) for node in nodes]
best_nodes_idx = self.weight_costs(
topo_costs,
[euclidean(node, map_info.robot_px) for node in nodes]
).argsort()[::-1] # We need descending order since we now want to maximize.
count = 0
for idx in best_nodes_idx:
node = nodes[idx]
path = map_info.create_path(node)
if path:
count += 1
yield node, path, topo_costs[idx]
if count == self.cost_based_properties['max_paths']:
break
if count == 0:
Print.art_print("Failed to create any path. Falling back to closer unoccupied.", Print.RED)
self.method = 'cost_based_fallback'
yield self.closer_node(map_info), None, None
@staticmethod
def cluster_nodes(nodes_original, robot_px, n_clusters):
Print.art_print("Trying to cluster:" + str(nodes_original), Print.BLUE)
whitened = whiten(nodes_original)
_, cluster_idx = kmeans2(whitened, n_clusters)
Print.art_print("Ended with clusters:" + str(cluster_idx), Print.BLUE)
keyfun = lambda x: cluster_idx[x[0]]
nodes = sorted(enumerate(nodes_original), key=keyfun)
for _, group in itertools.groupby(nodes, key=keyfun):
# For each cluster pick the farthest one.
max_idx = max(group, key=lambda x: euclidean(robot_px, x[1]))[0]
yield nodes_original[max_idx]
def weight_costs(self, *cost_vectors, **kwargs):
costs = self.normalize_costs(numpy.array(tuple(vector for vector in cost_vectors)))
if 'weights' in kwargs:
weights = kwargs['weights']
else:
weights = 2 ** numpy.arange(costs.shape[0] - 1, -1, -1)
return numpy.average(costs, axis=0, weights=weights)
@staticmethod
def closer_node(map_info):
robot_px = numpy.array(map_info.robot_px)
nodes = numpy.array(numpy.where(TargetSelection.is_good(
numpy.array(map_info.brush),
numpy.array(map_info.ogm),
numpy.array(map_info.coverage),
target=None
))).transpose()
closer_idx = numpy.linalg.norm(nodes - robot_px, axis=1).argmin()
return nodes[closer_idx]
def _topological_cost(self, node, ogm):
threshold = self.cost_based_properties['topo_threshold']
return self.topological_cost(node, ogm, threshold)
@staticmethod
def topological_cost(node, ogm, threshold):
result = 0
for dir_x, dir_y in [(0, 1), (1, 1), (1, 0), (1, -1), (0, -1), (-1, -1), (-1, 0), (-1, 1)]:
cost = 0
idx = 0
x, y = node
while cost < threshold:
idx += 1
x_c = x + idx * dir_x
y_c = y + idx * dir_y
cost = (x - x_c) ** 2 + (y - y_c) ** 2
if ogm[x_c][y_c] > 50:
break
elif ogm[x_c][y_c] in [50, -1]:
Print.art_print("{},{} is unknown!".format(x_c, y_c), Print.RED)
cost = threshold
break
result += min(threshold, cost)
return result
@staticmethod
def distance_cost(path, robot_px):
weighted_distances = (TargetSelection.distance(node1, node2) * TargetSelection.distance_coeff(node1, robot_px)
for node1, node2 in zip(path[:-1], path[1:]))
return sum(weighted_distances)
@staticmethod
def distance_coeff(node, robot_px, s=30, epsilon=0.0001):
x_n, y_n = node
x_g, y_g = robot_px
coeff = 1 - math.exp(-((x_n - x_g) ** 2 / (2 * s ** 2) + (y_n - y_g) ** 2 / (2 * s ** 2))) + epsilon
return 1 / coeff
@staticmethod
def distance(node_a, node_b):
x_1, y_1 = node_a
x_2, y_2 = node_b
return math.sqrt((x_1 - x_2) ** 2 + (y_1 - y_2) ** 2)
@staticmethod
def rotation_cost(path, robot_px, theta):
rotation = 0
rx, ry = robot_px
theta_old = theta
for node in path:
st_x, st_y = node
theta_new = math.atan2(st_y - ry, st_x - rx)
rotation += abs(theta_new - theta_old)
theta_old = theta_new
return rotation
@staticmethod
def coverage_cost(path, coverage):
coverage_sum = sum(coverage[x][y] for x, y in path)
return coverage_sum
@staticmethod
def normalize_costs(costs):
"""
:rtype: numpy.ndarray
"""
Print.art_print("Costs before normalization:\n" + str(costs), Print.BLUE)
assert (costs >= 0).all()
return 1 - (costs.transpose() / numpy.abs(costs).max(axis=1)).transpose()
class App(
Parameter,
Utils,
FieldView,
Draw,
Event,
):
def __init__(self):
super(Parameter, self).__init__()
super(Utils, self).__init__()
super(FieldView, self).__init__()
super(Draw, self).__init__()
self.capture = cv2.VideoCapture(camera)
self.capture.set(cv2.CAP_PROP_FRAME_WIDTH,
self.width) # カメラ画像の横幅を1280に設定
self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT,
self.height) # カメラ画像の縦幅を720に設定
self.table_set = Tables()
self.planner = PathPlanning(send=self.send)
self.table_detection = True
self.detection_success = False
self.bottle_result = [None, None, None]
self.standing_result_image = None
self.quit = False
self.yukari = Yukari()
self.remove_separator_middle = False
self.use_remove_separator = True
self.points = None
self.flip_points = None
self.set_track_bar_pos(self.settings)
self.ptlist = PointList(NPOINTS)
self.click_mode = True
logging.info('START DETECTION')
def get_param(self):
# スライダーの値を取得
self.h = cv2.getTrackbarPos('H', self.bar_window_name)
self.s = cv2.getTrackbarPos('S', self.bar_window_name)
self.v = cv2.getTrackbarPos('V', self.bar_window_name)
self.lv = cv2.getTrackbarPos('LV', self.bar_window_name)
self.th = cv2.getTrackbarPos('threshold', self.bar_window_name)
self.kn = cv2.getTrackbarPos('kernel', self.bar_window_name)
self.remove_side = cv2.getTrackbarPos('remove_side',
self.bar_window_name)
self.remove_side_e = cv2.getTrackbarPos('remove_side_e',
self.bar_window_name)
self.zone = cv2.getTrackbarPos('zone', self.bar_window_name)
def get_data_from_webcam(self) -> (np.asanyarray, None):
# ウェブカメラから画像データを取得
ret, frame = self.capture.read()
# frame = cv2.flip(frame, -1)
return frame, None
def get_data(self):
return self.get_data_from_webcam()
def remove_separator(self, color_image):
if self.click_mode:
return
if not self.use_remove_separator:
return
# セパレータ消すやつ
if self.zone:
if self.remove_separator_middle:
x = self.height
y = -(self.remove_side * 20)
y_x = y / x
f = lambda a: int(y_x * a - y)
pts = np.array([[0, 0], [self.remove_side * 20, 0],
[f(self.remove_side_e), self.remove_side_e],
[0, self.remove_side_e]])
cv2.fillPoly(color_image, pts=[pts], color=Color.red)
pts = np.array(
[[0, self.remove_side_e + 150],
[f(self.remove_side_e + 150), self.remove_side_e + 150],
[35, self.height], [0, self.height]])
cv2.fillPoly(color_image, pts=[pts], color=Color.red)
else:
pts = np.array([[0, 0], [self.remove_side * 20, 0],
[35, self.height], [0, self.height]])
cv2.fillPoly(color_image, pts=[pts], color=Color.red)
else:
if self.remove_separator_middle:
x = self.height
y = -(self.width - self.remove_side * 50)
y_x = y / x
f = lambda a: self.width - int(y_x * a - y)
pts = np.array([[self.remove_side * 50, 0],
[f(self.remove_side_e), self.remove_side_e],
[self.width, self.remove_side_e],
[self.width, 0]])
cv2.fillPoly(color_image, pts=[pts], color=Color.blue)
pts = np.array(
[[f(self.remove_side_e + 150), self.remove_side_e + 150],
[self.width, self.remove_side_e + 150],
[self.width, self.height]])
cv2.fillPoly(color_image, pts=[pts], color=Color.blue)
else:
pts = np.array([[self.remove_side * 50, 0], [self.width, 0],
[self.width, self.height]])
cv2.fillPoly(color_image, pts=[pts], color=Color.blue)
def draw(self, color_image_for_show, thresh):
# 画面描画
# 画面枠
if self.zone:
cv2.rectangle(color_image_for_show, (0, 0),
(self.width, self.height), Color.red, 20)
else:
cv2.rectangle(color_image_for_show, (0, 0),
(self.width, self.height), Color.blue, 20)
if self.detection_success:
# under tableを描画
color_image_for_show = self.put_info(color_image_for_show,
self.table_set.under)
# middle tableを描画
color_image_for_show = self.put_info(color_image_for_show,
self.table_set.middle)
# up tableを描画
color_image_for_show = self.put_info(color_image_for_show,
self.table_set.up)
# 二値をカラーに
thresh = cv2.applyColorMap(cv2.convertScaleAbs(thresh),
cv2.COLORMAP_BONE)
# threshウインドウのみthreshを表示
images_for_thresh = np.hstack((color_image_for_show, thresh))
if not self.table_detection:
# 立っているかの判定情報を描画
self.put_info_by_set(color_image_for_show, self.table_set,
Color.black)
self.standing_result_image = self.put_standing_detection_result(
color_image_for_show, self.table_set, self.bottle_result)
# ウインドウサイズがでかくなりすぎるので、縮小
images_for_thresh = cv2.resize(images_for_thresh,
(int(1280 * 0.65), int(480 * 0.65)))
# 表示
cv2.imshow(self.window_name, color_image_for_show)
cv2.imshow(self.bar_window_name, images_for_thresh)
cv2.setMouseCallback(
self.window_name, self.onMouse,
[self.window_name, color_image_for_show, self.ptlist])
def analyze(self):
# スライダーの値を取得
self.get_param()
# データを取得
color_image, np_depth = self.get_data()
# 画面に描画するようにcolor_imageをコピーした変数を作成
color_image_for_show = color_image.copy()
# 画像保存用にcolor_imageをコピーした変数を作成
self.color_image_for_save = color_image.copy()
# チェック用
for_check = color_image.copy()
# フィールドを分ける白いやつを消す
self.remove_separator(color_image)
self.remove_separator(color_image_for_show)
# ブラーをかける
color_image = cv2.medianBlur(color_image, 5)
# hsv空間に変換
hsv = cv2.cvtColor(color_image, cv2.COLOR_BGR2HSV)
# スライダーの値から白色の上限値、下限値を指定
upper_white = np.array([self.h, self.s, self.v])
lower_white = np.array([0, 0, self.lv])
# 白色でマスク
mask_white = cv2.inRange(hsv, lower_white, upper_white)
# 同じ部分だけ抽出
res_white = cv2.bitwise_and(color_image, color_image, mask=mask_white)
# グレースケールに変換
gray = cv2.cvtColor(res_white, cv2.COLOR_RGB2GRAY)
# 二値化
ret, thresh = cv2.threshold(gray, self.th, 255, cv2.THRESH_BINARY)
# 縮小と膨張
kernel = np.ones((self.kn, self.kn), np.uint8)
erode = cv2.erode(thresh, kernel)
thresh = cv2.dilate(erode, kernel)
# テーブルの検出処理
if self.table_detection:
# ペットボトル判定処理から戻ってきたときのためにFalseにする
self.processing_standing_detection = False
self.points, self.flip_points = None, None
tables = [] # テーブルの可能性がある輪郭をここに入れる
if self.click_mode:
self.draw_click(color_image_for_show, self.ptlist.get_points())
if self.ptlist.is_full():
for i, point in enumerate(self.ptlist.get_points()):
table = Table(point, (lambda x: 60 if x == 0 else
(70 if x == 1 else 100))(i), 0,
point)
tables.append(table)
else:
# 輪郭抽出
imgEdge, contours, hierarchy = cv2.findContours(
thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# 見つかった輪郭をリストに入れる
contours.sort(key=cv2.contourArea, reverse=True)
for cnt in contours:
t = self.calc_circle_level(cnt, cv2.contourArea(cnt))
if t > 0.8:
(x, y), radius = cv2.minEnclosingCircle(cnt)
center = (int(x), int(y))
radius = int(radius)
color_image_for_show = cv2.circle(
color_image_for_show, center, radius, (0, 255, 0),
2)
table = Table(center, radius, 0, (x, y))
if table.is_table(): # 本当にテーブルかチェック
tables.append(table) # テーブルだったらリストに追加
# 半径が大きい順にソート
tables = sorted(tables, reverse=True)
# 大きい3つだけを抽出
tables = tables[:3]
# Y座標が小さい順にソート
tables = sorted(tables, key=attrgetter('y'))
# 3つ見つかったら
self.detection_success = (len(tables) == 3) # Trueが成功
if self.detection_success:
self.table_set.update(tables[0], tables[1], tables[2])
# 検出処理が終わっていたら
else:
# ペットボトルが立っているかの検出
if self.use_standing_detection:
self.put_info_by_set(color_image_for_show, self.table_set,
Color.black)
self.put_standing_detection_result(color_image_for_show,
self.table_set,
self.bottle_result)
if not self.processing_standing_detection:
self.check_standing(for_check, self.table_set)
# self.table_set.reset_standing_result()
if not np.all(self.table_set.result is None):
play_result = []
if self.planner.shot[UNDER_NUM]:
if self.bottle_result[
UNDER_NUM] != self.table_set.result[
UNDER_NUM] and not (
self.bottle_result[UNDER_NUM] and
not self.table_set.result[UNDER_NUM]):
play_result.append(
[UNDER_NUM, self.table_set.result[UNDER_NUM]])
else:
self.table_set.under.standing = None
if self.planner.shot[MIDDLE_NUM]:
if self.bottle_result[
MIDDLE_NUM] != self.table_set.result[
MIDDLE_NUM] and not (
self.bottle_result[MIDDLE_NUM] and
not self.table_set.result[MIDDLE_NUM]):
play_result.append([
MIDDLE_NUM, self.table_set.result[MIDDLE_NUM]
])
else:
self.table_set.middle.standing = None
if self.planner.shot[UP_NUM]:
if self.bottle_result[UP_NUM] != self.table_set.result[
UP_NUM] and not (
self.bottle_result[UP_NUM]
and not self.table_set.result[UP_NUM]):
play_result.append(
[UP_NUM, self.table_set.result[UP_NUM]])
else:
self.table_set.up.standing = None
if play_result:
self.yukari.play_results(play_result)
self.bottle_result = self.table_set.result
# 立っていたらTrue、立っていなかったらFalse
self.planner.set_result_by_list(self.bottle_result)
if self.detection_success and not self.table_detection:
global timer
if time.time() - timer > 0.6: # 0.5秒に1回実行
# 画面内の座標を送信する座標に変換
ret = self.make_distance_to_send(self.table_set)
# 経路計画
if self.points is None:
self.points, self.flip_points = self.planner.main(
[ret.under, ret.middle, ret.up, self.zone])
if not self.planner.retry_start:
self.planner.send(self.points, self.flip_points, False)
# フィールド描画
field_view = self.draw_field(
(ret.under, ret.middle, ret.up), self.points)
cv2.imshow(self.field_window_name, field_view)
else:
retry_points, retry_flip_points = self.planner.retry(
(ret.under, ret.middle, ret.up, self.zone),
self.bottle_result)
self.planner.send(retry_points, retry_flip_points, True)
field_view = self.draw_retry(
(ret.under, ret.middle, ret.up), self.points,
retry_points, self.bottle_result)
cv2.imshow(self.field_window_name, field_view)
self.yukari.play_finish_path_planning()
timer = time.time()
# 描画
self.draw(color_image_for_show, thresh)
def key_check(self):
# キーの入力
key = cv2.waitKey(1)
# ペットボトル判定シーケンスに移行
if key == ord('n') and self.table_detection and self.detection_success:
# self.yukari.play_move_to_check_standing_sequence()
self.table_detection = False
logging.info('END DETECTION')
# テーブル検出シーケンスに移行
if key == ord('b') and not self.table_detection:
self.yukari.play_detecting_table()
self.table_detection = True
self.planner.retry_start = False
# 画像収集
if key == ord('r') and not self.table_detection:
global sc
logging.info(f'STORED:{sc}')
self.save_table_images(image=self.color_image_for_save,
table_set=self.table_set,
x_offset=20,
y_offset=20)
sc += 1
# 終了
if key == ord('q'):
logging.info('QUIT DETECTION')
self.quit = True
# パラメータの保存
if key == ord('s'):
logging.info('SAVED PARAMETER')
self.save_param(self.h, self.s, self.v, self.lv, self.th, self.kn,
self.remove_side)
if key == ord('e'):
# セパレータ削除における中央を消すかどうかの切り替え
self.remove_separator_middle = not self.remove_separator_middle
if key == ord('c'):
# クリックモード切替
self.click_mode = not self.click_mode
if key == ord('z'):
# クリックした座標をリセット
self.ptlist.reset_points()
if key == ord('i'):
# 強制二週目
self.planner.retry_start = True
if key == ord('f'):
# セパレータ削除の切り替え
self.use_remove_separator = not self.use_remove_separator
def run(self):
try:
while True:
try:
# 画像認識
self.analyze()
# キーボードの入力
self.key_check()
if self.quit:
break
except Exception as error:
logging.error(error)
except:
pass
class TargetSelection:
# Constructor
def __init__(self, selection_method):
self.goals_position = []
self.goals_value = []
self.omega = 0.0
self.radius = 0
self.method = selection_method
self.previous_target = [-1, -1]
self.brush = Brushfires()
self.topo = Topology()
self.path_planning = PathPlanning()
def selectTarget(self, init_ogm, coverage, robot_pose, origin, \
resolution, force_random = False):
target = [-1, -1]
######################### NOTE: QUESTION ##############################
# Implement a smart way to select the next target. You have the
# following tools: ogm_limits, Brushfire field, OGM skeleton,
# topological nodes.
# Find only the useful boundaries of OGM. Only there calculations
# have meaning
ogm_limits = OgmOperations.findUsefulBoundaries(init_ogm, origin, resolution)
# Blur the OGM to erase discontinuities due to laser rays
ogm = OgmOperations.blurUnoccupiedOgm(init_ogm, ogm_limits)
# Calculate Brushfire field
tinit = time.time()
brush = self.brush.obstaclesBrushfireCffi(ogm, ogm_limits)
Print.art_print("Brush time: " + str(time.time() - tinit), Print.ORANGE)
# Calculate skeletonization
tinit = time.time()
skeleton = self.topo.skeletonizationCffi(ogm, \
origin, resolution, ogm_limits)
Print.art_print("Skeletonization time: " + str(time.time() - tinit), Print.ORANGE)
# Find topological graph
tinit = time.time()
nodes = self.topo.topologicalNodes(ogm, skeleton, coverage, origin, \
resolution, brush, ogm_limits)
Print.art_print("Topo nodes time: " + str(time.time() - tinit), Print.ORANGE)
# Visualization of topological nodes
vis_nodes = []
for n in nodes:
vis_nodes.append([
n[0] * resolution + origin['x'],
n[1] * resolution + origin['y']
])
RvizHandler.printMarker(\
vis_nodes,\
1, # Type: Arrow
0, # Action: Add
"map", # Frame
"art_topological_nodes", # Namespace
[0.3, 0.4, 0.7, 0.5], # Color RGBA
0.1 # Scale
)
# Random point
if self.method == 'random' or force_random == True:
target = self.selectRandomTarget(ogm, coverage, brush, ogm_limits)
########################################################################
# Challenge 6. Smart point selection demands autonomous_expl.yaml->target_selector: 'smart'
# Smart point selection
if self.method == 'smart' and force_random == False:
nextTarget = self.selectSmartTarget(coverage, brush, robot_pose, resolution, origin, nodes)
# Check if selectSmartTarget found a target
if nextTarget is not None:
# Check if the next target is the same as the previous
dist = math.hypot( nextTarget[0] - self.previous_target[0], nextTarget[1] - self.previous_target[1])
if dist > 5:
target = nextTarget
else:
target = self.selectRandomTarget(ogm, coverage, brush, ogm_limits)
else:
# No target found. Choose a random
target = self.selectRandomTarget(ogm, coverage, brush, ogm_limits)
self.previous_target = target
return target
#############################################################################
# Challenge 6. select Smart Target Function
# this function follows the methodology presented
# on lecture 9.
def selectSmartTarget(self, coverage, brush, robot_pose, resolution, origin, nodes):
tinit = time.time()
# Get the robot pose in pixels
[rx, ry] = [int(round(robot_pose['x_px'] - origin['x'] / resolution)), int(round(robot_pose['y_px'] - origin['y'] / resolution))]
# Initialize weights matrix
weights = []
# Do procedure described in presentation 9
# for each node.
for i, node in enumerate(nodes):
# Calculate the path
path = np.flipud(self.path_planning.createPath([rx, ry], node, resolution))
# Check if it found a path
if path.shape[0] > 2:
# Vectors of the path
vectors = path[1:, :] - path[:-1, :]
# Calculate paths weighted distance
vectorsMean = vectors.mean(axis=0)
vectorsVar = vectors.var(axis=0)
dists = np.sqrt(np.einsum('ij,ij->i', vectors, vectors))
weightCoeff = 1 / (1 - np.exp(-np.sum((vectors - vectorsMean)**2 / (2 * vectorsVar), axis=1)) + 1e-4)
weightDists = np.sum(weightCoeff + dists)
# Topological weight
weightTopo = brush[node[0], node[1]]
# Cosine of the angles
c = np.sum(vectors[1:, :] * vectors[:-1, :], axis=1) / np.linalg.norm(vectors[1:, :], axis=1) / np.linalg.norm(vectors[:-1, :], axis=1)
# Sum of all angles
weightTurn = np.sum(abs(np.arccos(np.clip(c, -1, 1))))
# Calculate the coverage weight
pathIndex = np.rint(path).astype(int)
weightCove = 1 - np.sum(coverage[pathIndex[:, 0], pathIndex[:, 1]]) / (path.shape[0] * 255)
weights.append([i, weightDists, weightTopo, weightTurn, weightCove])
if len(weights) > 0:
weight = np.array(weights)
# Normalize the weights at [0,1]
weight[:,1:] = 1 - ((weight[:,1:] - np.min(weight[:,1:], axis=0)) / (np.max(weight[:,1:], axis=0) - np.min(weight[:,1:], axis=0)))
# Calculatete the final weights
finalWeights = 8 * weight[:, 2] + 4 * weight[:, 1] + 2 * weight[:, 4] + weight[:, 3]
# Find the best path
index = int(weight[max(xrange(len(finalWeights)), key=finalWeights.__getitem__)][0])
target = nodes[index]
Print.art_print("Smart target selection time: " + str(time.time() - tinit), Print.ORANGE)
return target
else:
Print.art_print("Smart target selection failed!!! Time: " + str(time.time() - tinit), Print.ORANGE)
return None
############################################################################
def selectRandomTarget(self, ogm, coverage, brushogm, ogm_limits):
# The next target in pixels
tinit = time.time()
next_target = [0, 0]
found = False
while not found:
x_rand = random.randint(0, ogm.shape[0] - 1)
y_rand = random.randint(0, ogm.shape[1] - 1)
if ogm[x_rand][y_rand] < 50 and coverage[x_rand][y_rand] < 50 and \
brushogm[x_rand][y_rand] > 5:
next_target = [x_rand, y_rand]
found = True
Print.art_print("Select random target time: " + str(time.time() - tinit), \
Print.ORANGE)
return next_target
class Navigation:
# Constructor
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.select_another_target = 0
self.inner_target_exists = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
self.count_limit = 200 # 20 sec
self.counter_to_next_sub = self.count_limit
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# Read the target function
self.target_selector = rospy.get_param("target_selector")
print "The selected target function is " + self.target_selector
self.target_selection = TargetSelection(self.target_selector)
# ROS Publisher for the path
self.path_publisher = \
rospy.Publisher(rospy.get_param('path_pub_topic'), \
Path, queue_size = 10)
# ROS Publisher for the subtargets
self.subtargets_publisher = \
rospy.Publisher(rospy.get_param('subgoals_pub_topic'),\
MarkerArray, queue_size = 10)
# ROS Publisher for the current target
self.current_target_publisher = \
rospy.Publisher(rospy.get_param('curr_target_pub_topic'),\
Marker, queue_size = 10)
def checkTarget(self, event):
# Check if we have a target or if the robot just wanders
if self.inner_target_exists == False or self.move_with_target == False or\
self.next_subtarget == len(self.subtargets):
return
self.counter_to_next_sub -= 1
if self.counter_to_next_sub == 0:
Print.art_print('\n~~~~ Time reset ~~~~', Print.RED)
self.inner_target_exists = False
self.target_exists = False
return
# Get the robot pose in pixels
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
# Clear visited subtargets
self.subtargets = self.subtargets[self.next_subtarget:]
self.next_subtarget = 0
# Calculate distance between the robot pose and all remaining subtargets
dx = [rx - st[0] for st in self.subtargets]
dy = [ry - st[1] for st in self.subtargets]
dist = [math.hypot(v[0], v[1]) for v in zip(dx, dy)]
# Check if any target is in distance
min_dist, min_idx = min(zip(dist, range(len(dist))))
######################### NOTE: QUESTION ##############################
# What if a later subtarget or the end has been reached before the
# next subtarget? Alter the code accordingly.
# Check if distance is less than 7 px (14 cm)
if min_dist < 5:
self.next_subtarget = min_idx + 1
self.counter_to_next_sub = self.count_limit
# Check if the final subtarget has been approached
if self.next_subtarget == len(self.subtargets):
self.target_exists = False
########################################################################
# Publish the current target
if self.next_subtarget >= len(self.subtargets):
return
subtarget = [\
self.subtargets[self.next_subtarget][0]\
* self.robot_perception.resolution + \
self.robot_perception.origin['x'],
self.subtargets[self.next_subtarget][1]\
* self.robot_perception.resolution + \
self.robot_perception.origin['y']\
]
RvizHandler.printMarker(\
[subtarget],\
1, # Type: Arrow
0, # Action: Add
"map", # Frame
"art_next_subtarget", # Namespace
[0, 0, 0.8, 0.8], # Color RGBA
0.2 # Scale
)
# Function that selects the next target, produces the path and updates
# the coverage field. This is called from the speeds assignment code, since
# it contains timer callbacks
def selectTarget(self):
# IMPORTANT: The robot must be stopped if you call this function until
# it is over
# Check if we have a map
while self.robot_perception.have_map == False:
Print.art_print("Navigation: No map yet", Print.RED)
return
print "\nClearing all markers"
RvizHandler.printMarker(\
[[0, 0]],\
1, # Type: Arrow
3, # Action: delete all
"map", # Frame
"null", # Namespace
[0,0,0,0], # Color RGBA
0.1 # Scale
)
print '\n\n----------------------------------------------------------'
print "Navigation: Producing new target"
# We are good to continue the exploration
# Make this true in order not to call it again from the speeds assignment
self.target_exists = True
# Gets copies of the map and coverage
local_ogm = self.robot_perception.getMap()
local_ros_ogm = self.robot_perception.getRosMap()
local_coverage = self.robot_perception.getCoverage()
print "Got the map and Coverage"
self.path_planning.setMap(local_ros_ogm)
# Once the target has been found, find the path to it
# Get the global robot pose
g_robot_pose = self.robot_perception.getGlobalCoordinates(\
[self.robot_perception.robot_pose['x_px'],\
self.robot_perception.robot_pose['y_px']])
# Call the target selection function to select the next best goal
# Choose target function
self.path = []
force_random = False
while len(self.path) == 0:
start = time.time()
target = self.target_selection.selectTarget(\
local_ogm,\
local_ros_ogm,\
local_coverage,\
self.robot_perception.robot_pose,
self.robot_perception.origin,
self.robot_perception.resolution,
force_random)
self.path = self.path_planning.createPath(\
g_robot_pose,\
target,
self.robot_perception.resolution)
print "Navigation: Path for target found with " + str(len(self.path)) +\
" points"
if len(self.path) == 0:
Print.art_print(\
"Path planning failed. Fallback to random target selection", \
Print.RED)
force_random = True
# Reverse the path to start from the robot
self.path = self.path[::-1]
# Smooth path
if len(self.path) > 3:
x = np.array(self.path)
y = np.copy(x)
a = 0.5
b = 0.2
e = np.sum(
np.abs(a * (x[1:-1, :] - y[1:-1, :]) + b *
(y[2:, :] - 2 * y[1:-1, :] + y[:-2, :])))
while e > 1e-3:
y[1:-1, :] += a * (x[1:-1, :] - y[1:-1, :]) + b * (
y[2:, :] - 2 * y[1:-1, :] + y[:-2, :])
e = np.sum(
np.abs(a * (x[1:-1, :] - y[1:-1, :]) + b *
(y[2:, :] - 2 * y[1:-1, :] + y[:-2, :])))
self.path = y.tolist()
# Break the path to subgoals every 2 pixels (1m = 20px)
step = 1
n_subgoals = (int)(len(self.path) / step)
self.subtargets = []
for i in range(0, n_subgoals):
self.subtargets.append(self.path[i * step])
self.subtargets.append(self.path[-1])
self.next_subtarget = 0
print "The path produced " + str(len(self.subtargets)) + " subgoals"
######################### NOTE: QUESTION ##############################
# The path is produced by an A* algorithm. This means that it is
# optimal in length but 1) not smooth (answer is just above)
# and 2) length optimality
# may not be desired for coverage-based exploration
########################################################################
self.counter_to_next_sub = self.count_limit
# Publish the path for visualization purposes
ros_path = Path()
ros_path.header.frame_id = "map"
for p in self.path:
ps = PoseStamped()
ps.header.frame_id = "map"
######################### NOTE: QUESTION ##############################
# Fill the ps.pose.position values to show the path in RViz
# You must understand what self.robot_perception.resolution
# and self.robot_perception.origin are.
ps.pose.position.x = p[
0] * self.robot_perception.resolution + self.robot_perception.origin[
'x']
ps.pose.position.y = p[
1] * self.robot_perception.resolution + self.robot_perception.origin[
'y']
########################################################################
ros_path.poses.append(ps)
self.path_publisher.publish(ros_path)
# Publish the subtargets for visualization purposes
subtargets_mark = []
for s in self.subtargets:
subt = [
s[0] * self.robot_perception.resolution + \
self.robot_perception.origin['x'],
s[1] * self.robot_perception.resolution + \
self.robot_perception.origin['y']
]
subtargets_mark.append(subt)
RvizHandler.printMarker(\
subtargets_mark,\
2, # Type: Sphere
0, # Action: Add
"map", # Frame
"art_subtargets", # Namespace
[0, 0.8, 0.0, 0.8], # Color RGBA
0.2 # Scale
)
self.inner_target_exists = True
def velocitiesToNextSubtarget(self):
[linear, angular] = [0, 0]
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
theta = self.robot_perception.robot_pose['th']
######################### NOTE: QUESTION ##############################
# The velocities of the robot regarding the next subtarget should be
# computed. The known parameters are the robot pose [x,y,th] from
# robot_perception and the next_subtarget [x,y]. From these, you can
# compute the robot velocities for the vehicle to approach the target.
# Hint: Trigonometry is required
l_max = 0.3
a_max = 0.3
if self.subtargets and self.next_subtarget <= len(self.subtargets) - 1:
st_x = self.subtargets[self.next_subtarget][0]
st_y = self.subtargets[self.next_subtarget][1]
# Fix robot turning problem
theta_r = np.arctan2(st_y - ry, st_x - rx)
dtheta = (theta_r - theta)
if dtheta > np.pi:
omega = (dtheta - 2 * np.pi) / np.pi
elif dtheta < -np.pi:
omega = (dtheta + 2 * np.pi) / np.pi
else:
omega = (theta_r - theta) / np.pi
# Velocities in [-0.3, 0.3] range
linear = l_max * ((1 - np.abs(omega))**4)
angular = a_max * np.sign(omega) * (abs(omega)**(1 / 3))
# omega = (theta_r - theta)/np.pi
# angular = omega * 0.3 # max angular = 0.3 rad/s
# linear_r = 1 - np.abs(omega) #may need square
# linear = linear_r * 0.3 # max linear = 0.3 m/s
######################### NOTE: QUESTION ##############################
return [linear, angular]
class Navigation:
# Constructor
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.target_selection = TargetSelection()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.inner_target_exists = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# ROS Publisher for the path
self.path_publisher = rospy.Publisher(rospy.get_param('path_pub_topic'), \
Path, queue_size = 10)
# ROS Publisher for the subtargets
self.subtargets_publisher = rospy.Publisher(rospy.get_param('subgoals_pub_topic'),\
MarkerArray, queue_size = 10)
# ROS Publisher for the current target
self.current_target_publisher = rospy.Publisher(rospy.get_param('curr_target_pub_topic'),\
Marker, queue_size = 10)
def checkTarget(self, event):
# Check if we have a target or if the robot just wanders
if self.inner_target_exists == False or self.move_with_target == False or\
self.next_subtarget == len(self.subtargets):
return
# Get the robot pose in pixels
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
# YOUR CODE HERE ------------------------------------------------------
# Here the next subtarget is checked for proximity. If the robot is too
# close to the subtarget it is considered approached and the next
# subtarget is assigned as next. However there are cases where the
# robot misses one subtarget due to obstacle detection. Enhance the below
# functionality by checking all the subgoals (and the final goal) for
# proximity and assign the proper next subgoal
# Find the distance between the robot pose and the next subtarget
dist = math.hypot(\
rx - self.subtargets[self.next_subtarget][0], \
ry - self.subtargets[self.next_subtarget][1])
# Check if distance is less than 7 px (14 cm)
if dist < 7:
print "Sub target reached!"
self.next_subtarget += 1
# Check if the final subtarget has been approached
if self.next_subtarget == len(self.subtargets):
print "Final goal reached!"
self.target_exists = False
# ---------------------------------------------------------------------
# Publish the current target
if self.next_subtarget == len(self.subtargets):
return
st = Marker()
st.header.frame_id = "map"
st.ns = 'as_curr_namespace'
st.id = 0
st.header.stamp = rospy.Time(0)
st.type = 1 # arrow
st.action = 0 # add
st.pose.position.x = self.subtargets[self.next_subtarget][0]\
* self.robot_perception.resolution + \
self.robot_perception.origin['x']
st.pose.position.y = self.subtargets[self.next_subtarget][1]\
* self.robot_perception.resolution + \
self.robot_perception.origin['y']
st.color.r = 0
st.color.g = 0
st.color.b = 0.8
st.color.a = 0.8
st.scale.x = 0.2
st.scale.y = 0.2
st.scale.z = 0.2
self.current_target_publisher.publish(st)
# Function that seelects the next target, produces the path and updates
# the coverage field. This is called from the speeds assignment code, since
# it contains timer callbacks
def selectTarget(self):
# IMPORTANT: The robot must be stopped if you call this function until
# it is over
# Check if we have a map
while self.robot_perception.have_map == False:
return
print "Navigation: Producing new target"
# We are good to continue the exploration
# Make this true in order not to call it again from the speeds assignment
self.target_exists = True
# Manually update the coverage field
self.robot_perception.updateCoverage()
print "Navigation: Coverage updated"
# Gets copies of the map and coverage
local_ogm = self.robot_perception.getMap()
local_coverage = self.robot_perception.getCoverage()
print "Got the map and Coverage"
# Call the target selection function to select the next best goal
target = self.target_selection.selectTarget(\
local_ogm,\
local_coverage,\
self.robot_perception.robot_pose)
print "Navigation: New target: " + str(target)
# Once the target has been found, find the path to it
# Get the global robot pose
g_robot_pose = self.robot_perception.getGlobalCoordinates(\
[self.robot_perception.robot_pose['x_px'],\
self.robot_perception.robot_pose['y_px']])
# Need to reverse the path??
self.path = self.path_planning.createPath(\
local_ogm,\
g_robot_pose,\
target)
print "Navigation: Path for target found with " + str(len(self.path)) +\
" points"
# Reverse the path to start from the robot
self.path = self.path[::-1]
# Break the path to subgoals every 10 pixels (0.5m)
step = 10
n_subgoals = (int)(len(self.path) / step)
self.subtargets = []
for i in range(0, n_subgoals):
self.subtargets.append(self.path[i * step])
self.subtargets.append(self.path[-1])
self.next_subtarget = 0
print "The path produced " + str(len(self.subtargets)) + " subgoals"
# Publish the path for visualization purposes
ros_path = Path()
ros_path.header.frame_id = "map"
for p in self.path:
ps = PoseStamped()
ps.header.frame_id = "map"
ps.pose.position.x = p[0] * self.robot_perception.resolution + \
self.robot_perception.origin['x']
ps.pose.position.y = p[1] * self.robot_perception.resolution + \
self.robot_perception.origin['y']
ros_path.poses.append(ps)
self.path_publisher.publish(ros_path)
# Publish the subtargets for visualization purposes
ros_subgoals = MarkerArray()
count = 0
for s in self.subtargets:
st = Marker()
st.header.frame_id = "map"
st.ns = 'as_namespace'
st.id = count
st.header.stamp = rospy.Time(0)
st.type = 2 # sphere
st.action = 0 # add
st.pose.position.x = s[0] * self.robot_perception.resolution + \
self.robot_perception.origin['x']
st.pose.position.y = s[1] * self.robot_perception.resolution + \
self.robot_perception.origin['y']
st.color.r = 0
st.color.g = 0.8
st.color.b = 0
st.color.a = 0.8
st.scale.x = 0.2
st.scale.y = 0.2
st.scale.z = 0.2
ros_subgoals.markers.append(st)
count += 1
self.subtargets_publisher.publish(ros_subgoals)
self.inner_target_exists = True
def velocitiesToNextSubtarget(self):
[linear, angular] = [0, 0]
# YOUR CODE HERE ------------------------------------------------------
# The velocities of the robot regarding the next subtarget should be
# computed. The known parameters are the robot pose [x,y,th] from
# robot_perception and the next_subtarget [x,y]. From these, you can
# compute the robot velocities for the vehicle to approach the target.
# Hint: Trigonometry is required
# ---------------------------------------------------------------------
return [linear, angular]
def __init__(self):
# Initializations of Objects Needed
self.robot_perception = RobotPerception()
self.path_planning = PathPlanning()
# Parameters from YAML File
self.move_with_target = rospy.get_param("calculate_target")
self.target_selector = rospy.get_param("target_selector")
self.turtlebot_path_topic = rospy.get_param('turtlebot_path_topic')
self.turtlebot_subgoals_topic = rospy.get_param(
'turtlebot_subgoals_topic')
self.turtlebot_curr_target_topic = rospy.get_param(
'turtlebot_curr_target_topic')
self.map_frame = rospy.get_param('map_frame')
self.debug = rospy.get_param('debug')
self.turtlebot_save_progress_images = rospy.get_param(
'turtlebot_save_progress_images')
self.limit_exploration_flag = rospy.get_param(
'limit_exploration') # Flag to Enable/ Disable OGM Enhancement
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.inner_target_exists = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
self.count_limit = 100 # 20 sec
self.counter_to_next_sub = self.count_limit
# Timer Thread (initialization)
self.time_limit = 150 # in seconds
self.timerThread = TimerThread(self.time_limit)
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# Read the target function
self.target_selection = TargetSelection(self.target_selector)
# ROS Publisher for the path
self.path_publisher = rospy.Publisher(self.turtlebot_path_topic,
Path,
queue_size=10)
# ROS Publisher for the subtargets
self.subtargets_publisher = rospy.Publisher(
self.turtlebot_subgoals_topic, MarkerArray, queue_size=10)
# ROS Publisher for the current target
self.current_target_publisher = rospy.Publisher(
self.turtlebot_curr_target_topic, Marker, queue_size=10)
# For Testing Purpose
self.timer_flag = True # True to Enable Timer for resetting Navigation
# Speed Parameter
self.max_linear_velocity = rospy.get_param('max_linear_speed')
self.max_angular_velocity = rospy.get_param('max_angular_speed')
# Parameters of OGM Enhancement
self.first_run_flag = True # Flag to Control first Enhancement in OGM Map
# Map Container Visualize Message
self.map_size = np.array(
[[rospy.get_param('x_min_size'),
rospy.get_param('y_min_size')],
[rospy.get_param('x_min_size'),
rospy.get_param('y_max_size')],
[rospy.get_param('x_max_size'),
rospy.get_param('y_min_size')],
[rospy.get_param('x_max_size'),
rospy.get_param('y_max_size')]],
dtype=np.float64)
if self.limit_exploration_flag:
# Create CV Bridge Object
self.bridge = CvBridge()
# Parameters
self.drone_image = [] # Hold the Drone OGM Image converted in CV!
self.ogm_in_cv = [] # Hold the OGM in CV Compatible Version
self.drone_yaw = 0.0 # Holds the current Drone Yaw
self.drone_map_pose = [] # Holds the current Drone Position in Map
# Service
self.drone_explore_limit_srv_name = rospy.get_param(
'drone_explore_limit_srv')
self.limits_exploration_service = rospy.ServiceProxy(
self.drone_explore_limit_srv_name, OgmLimits)
# Subscriber
self.drone_pub_image_topic = rospy.get_param(
'drone_pub_image_topic')
self.drone_image_sub = rospy.Subscriber(self.drone_pub_image_topic,
Image, self.drone_image_cb)
# Flags
self.done_enhancement = False # Flag for Controlling OGM Enhancement Loop
self.drone_take_image = False # Flag for Controlling When to Read Image From Drone Camera
# FIXME: Testing
self.match_with_limit_pose = True # True/False to Match with Known Location/Template Matching
self.match_with_drone_pose = False # True if you match with Drone Real Pose
class Navigation:
# Constructor
def __init__(self):
# Initializations
self.robot_perception = RobotPerception()
self.path_planning = PathPlanning()
# Check if the robot moves with target or just wanders
self.move_with_target = rospy.get_param("calculate_target")
# Flag to check if the vehicle has a target or not
self.target_exists = False
self.select_another_target = 0
self.inner_target_exists = False
# Container for the current path
self.path = []
# Container for the subgoals in the path
self.subtargets = []
# Container for the next subtarget. Holds the index of the next subtarget
self.next_subtarget = 0
self.count_limit = 400 # 20 sec
self.counter_to_next_sub = self.count_limit
# Check if subgoal is reached via a timer callback
rospy.Timer(rospy.Duration(0.10), self.checkTarget)
# Read the target function
self.target_selector = rospy.get_param("target_selector")
print "The selected target function is " + self.target_selector
self.target_selection = TargetSelection(self.target_selector)
# ROS Publisher for the path
self.path_publisher = \
rospy.Publisher(rospy.get_param('path_pub_topic'), \
Path, queue_size = 10)
# ROS Publisher for the subtargets
self.subtargets_publisher = \
rospy.Publisher(rospy.get_param('subgoals_pub_topic'),\
MarkerArray, queue_size = 10)
# ROS Publisher for the current target
self.current_target_publisher = \
rospy.Publisher(rospy.get_param('curr_target_pub_topic'),\
Marker, queue_size = 10)
def checkTarget(self, event):
# Check if we have a target or if the robot just wanders
if self.inner_target_exists == False or self.move_with_target == False or\
self.next_subtarget == len(self.subtargets):
return
self.counter_to_next_sub -= 1
if self.counter_to_next_sub == 0:
Print.art_print('\n~~~~ Time reset ~~~~', Print.RED)
self.inner_target_exists = False
self.target_exists = False
return
# Get the robot pose in pixels
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
for i in range(self.next_subtarget, len(self.subtargets)):
# Find the distance between the robot pose and the next subtarget
dist = math.hypot(\
rx - self.subtargets[i][0], \
ry - self.subtargets[i][1])
######################### NOTE: QUESTION ##############################
# What if a later subtarget or the end has been reached before the
# next subtarget? Alter the code accordingly.
# Check if distance is less than 7 px (14 cm)
if dist < 7:
self.next_subtarget = 1 + i
self.counter_to_next_sub = self.count_limit
# Check if the final subtarget has been approached
if self.next_subtarget == len(self.subtargets):
self.target_exists = False
########################################################################
# Publish the current target
if self.next_subtarget == len(self.subtargets):
return
subtarget = [\
self.subtargets[self.next_subtarget][0]\
* self.robot_perception.resolution + \
self.robot_perception.origin['x'],
self.subtargets[self.next_subtarget][1]\
* self.robot_perception.resolution + \
self.robot_perception.origin['y']\
]
RvizHandler.printMarker(\
[subtarget],\
1, # Type: Arrow
0, # Action: Add
"map", # Frame
"art_next_subtarget", # Namespace
[0, 0, 0.8, 0.8], # Color RGBA
0.2 # Scale
)
# Function that selects the next target, produces the path and updates
# the coverage field. This is called from the speeds assignment code, since
# it contains timer callbacks
def selectTarget(self):
# IMPORTANT: The robot must be stopped if you call this function until
# it is over
# Check if we have a map
while self.robot_perception.have_map == False:
Print.art_print("Navigation: No map yet", Print.RED)
return
print "\nClearing all markers"
RvizHandler.printMarker(\
[[0, 0]],\
1, # Type: Arrow
3, # Action: delete all
"map", # Frame
"null", # Namespace
[0,0,0,0], # Color RGBA
0.1 # Scale
)
print '\n\n----------------------------------------------------------'
print "Navigation: Producing new target"
# We are good to continue the exploration
# Make this true in order not to call it again from the speeds assignment
self.target_exists = True
# Gets copies of the map and coverage
local_ogm = self.robot_perception.getMap()
local_ros_ogm = self.robot_perception.getRosMap()
local_coverage = self.robot_perception.getCoverage()
print "Got the map and Coverage"
self.path_planning.setMap(local_ros_ogm)
# Once the target has been found, find the path to it
# Get the global robot pose
g_robot_pose = self.robot_perception.getGlobalCoordinates(\
[self.robot_perception.robot_pose['x_px'],\
self.robot_perception.robot_pose['y_px']])
# Call the target selection function to select the next best goal
# Choose target function
self.path = []
force_random = False
while len(self.path) == 0:
start = time.time()
target = self.target_selection.selectTarget(\
local_ogm,\
local_coverage,\
self.robot_perception.robot_pose,
self.robot_perception.origin,
self.robot_perception.resolution,
force_random)
self.path = self.path_planning.createPath(\
g_robot_pose,\
target,
self.robot_perception.resolution)
print "Navigation: Path for target found with " + str(len(self.path)) +\
" points"
if len(self.path) == 0:
Print.art_print(\
"Path planning failed. Fallback to random target selection", \
Print.RED)
force_random = True
# Reverse the path to start from the robot
self.path = self.path[::-1]
# Break the path to subgoals every 2 pixels (1m = 20px)
step = 1
n_subgoals = (int)(len(self.path) / step)
self.subtargets = []
for i in range(0, n_subgoals):
self.subtargets.append(self.path[i * step])
self.subtargets.append(self.path[-1])
self.next_subtarget = 0
print "The path produced " + str(len(self.subtargets)) + " subgoals"
######################### NOTE: QUESTION ##############################
# The path is produced by an A* algorithm. This means that it is
# optimal in length but 1) not smooth and 2) length optimality
# may not be desired for coverage-based exploration
########################################################################
self.counter_to_next_sub = self.count_limit
# Publish the path for visualization purposes
ros_path = Path()
ros_path.header.frame_id = "map"
for p in self.path:
ps = PoseStamped()
ps.header.frame_id = "map"
ps.pose.position.x = 0
ps.pose.position.y = 0
######################### NOTE: QUESTION ##############################
# Fill the ps.pose.position values to show the path in RViz
# You must understand what self.robot_perception.resolution
# and self.robot_perception.origin arE
ps.pose.position.x = self.robot_perception.origin['x'] /\
self.robot_perception.resolution
ps.pose.position.y = self.robot_perception.origin['y'] /\
self.robot_perception.resolution
ros_path.poses.append(ps)
self.path_publisher.publish(ros_path)
# Publish the subtargets for visualization purposes
subtargets_mark = []
for s in self.subtargets:
subt = [
s[0] * self.robot_perception.resolution + \
self.robot_perception.origin['x'],
s[1] * self.robot_perception.resolution + \
self.robot_perception.origin['y']
]
subtargets_mark.append(subt)
RvizHandler.printMarker(\
subtargets_mark,\
2, # Type: Sphere
0, # Action: Add
"map", # Frame
"art_subtargets", # Namespace
[0, 0.8, 0.0, 0.8], # Color RGBA
0.2 # Scale
)
self.inner_target_exists = True
def velocitiesToNextSubtarget(self):
[linear, angular] = [0, 0]
[rx, ry] = [\
self.robot_perception.robot_pose['x_px'] - \
self.robot_perception.origin['x'] / self.robot_perception.resolution,\
self.robot_perception.robot_pose['y_px'] - \
self.robot_perception.origin['y'] / self.robot_perception.resolution\
]
theta = self.robot_perception.robot_pose['th']
######################### NOTE: QUESTION ##############################
# The velocities of the robot regarding the next subtarget should be
# computed. The known parameters are the robot pose [x,y,th] from
# robot_perception and the next_subtarget [x,y]. From these, you can
# compute the robot velocities for the vehicle to approach the target.
# Hint: Trigonometry is required
if self.subtargets and self.next_subtarget <= len(self.subtargets) - 1:
st_x = self.subtargets[self.next_subtarget][0]
st_y = self.subtargets[self.next_subtarget][1]
wmega = math.atan2((st_y - ry), (st_x - rx)) - theta
print(wmega)
print(math.atan2((st_y - ry), (st_x - rx)))
print(theta)
if abs(wmega) >= 0.1:
angular = np.sign(wmega) * 0.3 #(wmega/(3.14))*0.3
linear = 0 #(1-(wmega/(3.14)))*0.3
else:
angular = (wmega / (3.14)) * 0.3
linear = math.tanh(
math.sqrt(pow((st_y - ry), 2) + pow((st_x - rx), 2))) * 0.3
######################### NOTE: QUESTION ##############################
print(angular)
return [linear, angular]
class TargetSelection:
# Constructor
def __init__(self, selection_method):
self.initial_time = time()
self.method = selection_method
self.initialize_gains = False
self.brush = Brushfires()
self.topology = Topology()
self.path_planning = PathPlanning()
self.droneConnector = DroneCommunication()
# Parameters from YAML File
self.debug = True #rospy.get_param('debug')
self.map_discovery_purpose = rospy.get_param('map_discovery_purpose')
self.color_evaluation_flag = rospy.get_param('color_rating')
self.drone_color_evaluation_topic = rospy.get_param(
'drone_pub_color_rating')
self.evaluate_potential_targets_srv_name = rospy.get_param(
'rate_potential_targets_srv')
# Explore Gains
self.g_color = 0.0
self.g_brush = 0.0
self.g_corner = 0.0
self.g_distance = 0.0
self.set_gain()
if self.color_evaluation_flag:
# Color Evaluation Service
self.color_evaluation_service = rospy.ServiceProxy(
self.evaluate_potential_targets_srv_name, EvaluateTargets)
# Subscribe to Color Evaluation Topic to Get Results from Color Evaluation
self.drone_color_evaluation_sub = rospy.Subscriber(
self.drone_color_evaluation_topic, ColorEvaluationArray,
self.color_evaluation_cb)
# Parameters
self.targets_color_evaluated = False # Set True Once Color Evaluation of Targets Completed
self.color_evaluation = [] # Holds the Color Evaluation of Targets
self.corner_evaluation = [
] # Holds the Number of Corners Near Each Target
# Target Selection Function
def selectTarget(self,
init_ogm,
coverage,
robot_pose,
origin,
resolution,
g_robot_pose,
previous_limits=[],
force_random=False):
# Initialize Target
target = [-1, -1]
if self.running_time() > 15:
print('\x1b[37;1m' + str('15 Minutes Constraint Passed!!!') +
'\x1b[0m')
# Find only the useful boundaries of OGM
start = time()
ogm_limits = OgmOperations.findUsefulBoundaries(init_ogm,
origin,
resolution,
print_result=True,
step=20)
if self.debug:
print('\x1b[34;1m' + str('Target Selection: OGM Boundaries ') +
str(ogm_limits) + str(' in ') + str(time() - start) +
str(' seconds.') + '\x1b[0m')
# Blur the OGM to erase discontinuities due to laser rays
start = time()
ogm = OgmOperations.blurUnoccupiedOgm(init_ogm, ogm_limits)
if self.debug:
print('\x1b[34;1m' + str('Target Selection: OGM Blurred in ') +
str(time() - start) + str(' seconds.') + '\x1b[0m')
# Calculate Brushfire field
start = time()
brush = self.brush.obstaclesBrushfireCffi(ogm, ogm_limits)
if self.debug:
print('\x1b[34;1m' + str('Target Selection: Brush in ') +
str(time() - start) + str(' seconds.') + '\x1b[0m')
# Calculate Robot Position
[rx, ry] = [
robot_pose['x_px'] - origin['x'] / resolution,
robot_pose['y_px'] - origin['y'] / resolution
]
# Calculate Skeletonization
start = time()
skeleton = self.topology.skeletonizationCffi(ogm, origin, resolution,
ogm_limits)
if self.debug:
print('\x1b[34;1m' + str('Target Selection: Skeletonization in ') +
str(time() - start) + str(' seconds.') + '\x1b[0m')
# Find Topological Graph
start = time()
potential_targets = self.topology.topologicalNodes(
ogm,
skeleton,
coverage,
brush,
final_num_of_nodes=25,
erase_distance=100,
step=15)
if self.debug:
print('\x1b[34;1m' +
str('Target Selection: Topological Graph in ') +
str(time() - start) + str(' seconds.') + '\x1b[0m')
print('\x1b[34;1m' +
str("The Potential Targets to be Checked are ") +
str(len(potential_targets)) + '\x1b[0m')
if len(potential_targets) == 0:
print('\x1b[32;1m' +
str('\n------------------------------------------') +
str("Finished Space Exploration!!! ") +
str('------------------------------------------\n') +
'\x1b[0m')
sleep(10000)
# Visualization of Topological Graph
vis__potential_targets = []
for n in potential_targets:
vis__potential_targets.append([
n[0] * resolution + origin['x'],
n[1] * resolution + origin['y']
])
RvizHandler.printMarker(vis__potential_targets, 1, 0, "map",
"art_topological_nodes", [0.3, 0.4, 0.7, 0.5],
0.1)
# Check if we have given values to Gains
if not self.initialize_gains:
self.set_gain()
# Random Point Selection if Needed
if self.method == 'random' or force_random:
# Get Distance from Potential Targets
distance = np.zeros((len(potential_targets), 1), dtype=np.float32)
for idx, target in enumerate(potential_targets):
distance[idx] = hypot(rx - target[0], ry - target[1])
distance *= 255.0 / distance.max()
path = self.selectRandomTarget(ogm, coverage, brush, ogm_limits,
potential_targets, distance,
resolution, g_robot_pose)
if path is not None:
return path
else:
return []
# Sent Potential Targets for Color Evaluation (if Flag is Enable)
if self.color_evaluation_flag:
start_color = time()
while not self.sent_potential_targets_for_color_evaluation(
potential_targets, resolution, origin):
pass
# Initialize Arrays for Target Selection
id = np.array(range(0, len(potential_targets))).reshape(-1, 1)
brushfire = np.zeros((len(potential_targets), 1), dtype=np.float32)
distance = np.zeros((len(potential_targets), 1), dtype=np.float32)
color = np.zeros((len(potential_targets), 1), dtype=np.float32)
corners = np.zeros((len(potential_targets), 1), dtype=np.float32)
score = np.zeros((len(potential_targets), 1), dtype=np.float32)
# Calculate Distance and Brush Evaluation
start = time()
for idx, target in enumerate(potential_targets):
distance[idx] = hypot(rx - target[0], ry - target[1])
brushfire[idx] = brush[target[0], target[1]]
if self.debug:
print('\x1b[35;1m' +
str('Distance and Brush Evaluation Calculated in ') +
str(time() - start) + str(' seconds.') + '\x1b[0m')
# Wait for Color Evaluation to be Completed
if self.color_evaluation_flag:
while not self.targets_color_evaluated:
pass
color = np.array(self.color_evaluation).reshape(-1, 1)
corners = np.array(self.corner_evaluation,
dtype=np.float64).reshape(-1, 1)
# Reset Flag for Next Run
self.targets_color_evaluated = False
if self.debug:
print('\x1b[35;1m' + str('Color Evaluation Calculated in ') +
str(time() - start_color) + str(' seconds.') + '\x1b[0m')
# Normalize Evaluation Arrays to [0, 255]
distance *= 255.0 / distance.max()
brushfire *= 255.0 / brushfire.max()
if self.color_evaluation_flag:
# color max is 640*320 = 204800
color *= 255.0 / color.max()
color = 255.0 - color
corners *= 255.0 / corners.max()
# Calculate Score to Choose Best Target
# Final Array = [ Id. | [X] | [Y] | Color | Brush | Dist. | Num. of Corners | Score ]
# 0 1 2 3 4 5 6 7
# Max is: 255 + 255 - 0 - 0 = +510
# Min is: 0 + 0 - 255 - 255 = -510
evaluation = np.concatenate((id, potential_targets, color, brushfire,
distance, corners, score),
axis=1)
for e in evaluation:
# Choose Gains According to Type of Exploration (Default is Exploration)
if self.map_discovery_purpose == 'coverage':
e[7] = self.g_color * e[3] + self.g_brush * e[
4] - self.g_distance * e[5] - self.g_corner * e[6]
elif self.map_discovery_purpose == 'combined':
# Gains Change over Time
self.set_gain()
e[7] = self.g_color * e[3] + self.g_brush * e[
4] - self.g_distance * e[5] - self.g_corner * e[6]
else:
e[7] = self.g_color * e[3] + self.g_brush * e[
4] - self.g_distance * e[5] - self.g_corner * e[6]
# Normalize Score to [0, 255] and Sort According to Best Score (Increasingly)
evaluation[:, 7] = self.rescale(
evaluation[:,
7], (-self.g_distance * 255.0 - self.g_corner * 255.0),
(self.g_color * 255.0 + self.g_brush * 255.0), 0.0, 255.0)
evaluation = evaluation[evaluation[:, 7].argsort()]
# Best Target is in the Bottom of Evaluation Table Now
target = [
evaluation[[len(potential_targets) - 1], [1]],
evaluation[[len(potential_targets) - 1], [2]]
]
# Print The Score of the Target Selected
if len(previous_limits) != 0:
if not previous_limits['min_x'] < target[0] < previous_limits['max_x'] and not\
previous_limits['min_y'] < target[1] < previous_limits['max_y']:
print('\x1b[38;1m' +
str('Selected Target was inside Explored Area.') +
'\x1b[0m')
print('\x1b[35;1m' + str('Selected Target was ') +
str(int(evaluation.item(
(len(potential_targets) - 1), 0))) + str(' with score of ') +
str(evaluation.item(
(len(potential_targets) - 1), 7)) + str('.') + '\x1b[0m')
return self.path_planning.createPath(g_robot_pose, target, resolution)
# Send SIGNAL to Drone to Color Evaluate Potential Targets Function
def sent_potential_targets_for_color_evaluation(self, targets, resolution,
origin):
# Calculate Position of Targets in Map
targets = np.asarray(targets, dtype=np.float64)
for target in targets:
target[0] = target[0] * resolution + origin['x']
target[1] = target[1] * resolution + origin['y']
# Create Color Evaluation Request Message
color_evaluation_request_msg = EvaluateTargetsRequest()
color_evaluation_request_msg.pos_x = np.array(targets[:, 0])
color_evaluation_request_msg.pos_y = np.array(targets[:, 1])
# Call Service
rospy.wait_for_service(self.evaluate_potential_targets_srv_name)
try:
response = self.color_evaluation_service(
color_evaluation_request_msg)
except rospy.ServiceException, e:
print "Service call failed: %s" % e
return response