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
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()
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
)
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
# Check if timer has expired
if self.timer_flag:
if self.timerThread.expired:
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
# If distance between the robot pose and the next subtarget is < 7 pixel consider that Target is Reached
dist = math.hypot(rx - self.subtargets[self.next_subtarget][0],
ry - self.subtargets[self.next_subtarget][1])
if dist < 7:
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, 0, "map", "art_next_subtarget",
[0, 0, 0.8, 0.8], 0.2)
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 selectFurthestTarget(self, ogm, coverage, brushogm, ogm_limits, nodes):
# The next target in pixels
tinit = time.time()
next_target = [0, 0]
for i in range(len(nodes) - 1, 0, -1):
x = nodes[i][0]
y = nodes[i][1]
if ogm[x][y] < 50 and coverage[x][y] < 50 and \
brushogm[x][y] > 10:
next_target = [x, y]
Print.art_print("Select furthest target time: " + str(time.time() - tinit), \
Print.ORANGE)
return next_target
return self.selectRandomTarget(ogm, coverage, brushogm, ogm_limits)
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
def TargetSel(self, ogm, coverage, brushogm, ogm_limits):
tinit = time.time()
next_target = [0, 0]
a = []
for x in range(0, ogm.shape[0]):
for y in range(0, ogm.shape[1]):
temp = (brushogm[x][y] - 5) * 0.6 + (ogm[x][y] - 50) * 0.2 + (
coverage[x][y] - 50) * 0.2
a.append(temp)
c = a.index(min(a))
if ogm.shape[0] >= ogm.shape[1]:
next_target = [c % ogm.shape[0], c / ogm.shape[0]]
else:
next_target = [c / ogm.shape[0], c % ogm.shape[0]]
Print.art_print("Select target time: " + str(time.time() - tinit), \
Print.ORANGE)
return next_target
def selectNearestTopologyNode(self, robot_pose, resolution, nodes):
# The next target in pixels
tinit = time.time()
next_target = [0, 0]
w_dist = 10000
x_g, y_g = 500, 500
sigma = 100
g_robot_pose = self.robot_perception.getGlobalCoordinates(
[robot_pose['x_px'], robot_pose['y_px']]) # can I use that?
for node in nodes:
# print resolution
self.path = self.path_planning.createPath(
g_robot_pose, node, resolution) # can I use that?
if len(self.path) == 0:
break
x_n, y_n = node[0], node[1]
exp = ((x_n - x_g)**2 + (y_n - y_g)**2) / (2 * (sigma**2))
scale_factor = 1 / (1 - math.exp(-exp) + 0.01)
coords = zip(
*self.path) # dist is [[x1 x2 x3..],[y1 y2 y3 ..]] #transpose
coord_shift = np.empty_like(coords)
coord_shift[0] = coords[0][-1:] + coords[0][:-1] # (xpi+1)
coord_shift[1] = coords[1][-1:] + coords[1][:-1] # (ypi+1)
coords = np.asarray(coords)
dist = [
(a - b)**2 for a, b in zip(coords[:, 1:], coord_shift[:, 1:])
] # (xpi - xpi+1)^2 , (ypi-ypi+1)^2
dist = sum(np.sqrt(dist[0] + dist[1]))
w = dist * (1 / scale_factor)
if w < w_dist and len(self.path) != 0:
if self.prev_target == node:
break
w_dist = w
next_target = node
self.prev_target = next_target # dont select the same target it we failed already
Print.art_print(
"Select nearest node target time: " + str(time.time() - tinit),
Print.ORANGE)
return next_target
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 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
def MINTargetSel(self, ogm, coverage, brushogm, ogm_limits, nodes, x, y):
tinit = time.time()
next_target = [0, 0]
a = []
b = []
d = []
print(x)
print(y)
for n in nodes:
#The min distance topo node from robot
temp = -(math.pow(x - n[0], 2) + math.pow(y - n[1], 2))
a.append(temp)
b.append(n[0])
d.append(n[1])
c = a.index(max(a))
next_target = [b[c], d[c]]
Print.art_print("Select MIN target time: " + str(time.time() - tinit), \
Print.ORANGE)
print(next_target)
return next_target
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)
########################################################################
return target
def createPath(self, robot_pose, target_pose, resolution):
# Ask for path
resp = OgmppPathPlanningSrvResponse()
req = OgmppPathPlanningSrvRequest()
req.data.begin = Point()
req.data.end = Point()
req.data.begin.x = robot_pose[0] * resolution
req.data.begin.y = robot_pose[1] * resolution
req.data.end.x = target_pose[0] * resolution
req.data.end.y = target_pose[1] * resolution
req.method = "uniform_prm"
tinit = time.time()
resp = self.ogmpp_pp(req)
Print.art_print("Path planning time: " + str(time.time() - tinit), Print.ORANGE)
path = []
for p in resp.path.poses:
path.append([p.pose.position.x / resolution,
p.pose.position.y / resolution])
return path
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 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
)
def selectTarget(self, ogm, coverage, robot_pose, origin, \
resolution, force_random = False):
# Random point
if self.method == 'random' or force_random == True:
init_ogm = ogm
# 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
)
target = self.selectRandomTarget(ogm, coverage, brush, ogm_limits)
return [False, target]
tinit = time.time()
g_robot_pose = [robot_pose['x_px'] - int(origin['x'] / resolution), \
robot_pose['y_px'] - int(origin['y'] / resolution)]
# Calculate coverage frontier with sobel filters
tinit = time.time()
cov_dx = scipy.ndimage.sobel(coverage, 0)
cov_dy = scipy.ndimage.sobel(coverage, 1)
cov_frontier = np.hypot(cov_dx, cov_dy)
cov_frontier *= 100 / np.max(cov_frontier)
cov_frontier = 100 * (cov_frontier > 80)
Print.art_print("Sobel filters time: " + str(time.time() - tinit),
Print.BLUE)
# Remove the edges that correspond to obstacles instead of frontiers (in a 5x5 radius)
kern = 5
tinit = time.time()
i_rng = np.matlib.repmat(
np.arange(-(kern / 2), kern / 2 + 1).reshape(kern, 1), 1, kern)
j_rng = np.matlib.repmat(np.arange(-(kern / 2), kern / 2 + 1), kern, 1)
for i in range((kern / 2), cov_frontier.shape[0] - (kern / 2)):
for j in range((kern / 2), cov_frontier.shape[1] - (kern / 2)):
if cov_frontier[i, j] == 100:
if np.any(ogm[i + i_rng, j + j_rng] > 99):
cov_frontier[i, j] = 0
Print.art_print("Frontier trimming time: " + str(time.time() - tinit),
Print.BLUE)
# Save coverage frontier as image (for debugging purposes)
# scipy.misc.imsave('test.png', np.rot90(cov_frontier))
# Frontier detection/grouping
tinit = time.time()
labeled_frontiers, num_frontiers = scipy.ndimage.label(
cov_frontier, np.ones((3, 3)))
Print.art_print("Frontier grouping time: " + str(time.time() - tinit),
Print.BLUE)
goals = np.full((num_frontiers, 2), -1)
w_dist = np.full(len(goals), -1)
w_turn = np.full(len(goals), -1)
w_size = np.full(len(goals), -1)
w_obst = np.full(len(goals), -1)
# Calculate the centroid and its cost, for each frontier
for i in range(1, num_frontiers + 1):
points = np.where(labeled_frontiers == i)
# Discard small groupings (we chose 20 as a treshold arbitrarily)
group_length = points[0].size
if group_length < 20:
labeled_frontiers[points] = 0
continue
sum_x = np.sum(points[0])
sum_y = np.sum(points[1])
centroid = np.array([sum_x / group_length,
sum_y / group_length]).reshape(2, 1)
# Find the point on the frontier nearest (2-norm) to the centroid, and use it as goal
nearest_idx = np.linalg.norm(np.array(points) - centroid,
axis=0).argmin()
print ogm[int(points[0][nearest_idx]), int(points[1][nearest_idx])]
goals[i - 1, :] = np.array(
[points[0][nearest_idx], points[1][nearest_idx]])
# Save centroids for later visualisation (for debugging purposes)
labeled_frontiers[int(goals[i - 1, 0]) + i_rng,
int(goals[i - 1, 1]) + j_rng] = i
# Calculate size of obstacles between robot and goal
line_pxls = list(bresenham(int(goals[i-1,0]), int(goals[i-1,1]),\
g_robot_pose[0], g_robot_pose[1]))
ogm_line = list(map(lambda pxl: ogm[pxl[0], pxl[1]], line_pxls))
N_occupied = len(list(filter(lambda x: x > 25, ogm_line)))
N_line = len(line_pxls)
w_obst[i - 1] = float(N_occupied) / N_line
# print('Occupied = '+str(N_occupied))
# print('Full Line = '+str(N_line))
# ipdb.set_trace()
# Manhattan distance
w_dist[i - 1] = scipy.spatial.distance.cityblock(
goals[i - 1, :], g_robot_pose)
# Missalignment
theta = np.arctan2(goals[i - 1, 1] - g_robot_pose[1],
goals[i - 1, 0] - g_robot_pose[0])
w_turn[i - 1] = (theta - robot_pose['th'])
if w_turn[i - 1] > np.pi:
w_turn[i - 1] -= 2 * np.pi
elif w_turn[i - 1] < -np.pi:
w_turn[i - 1] += 2 * np.pi
# We don't care about the missalignment direction so we abs() it
w_turn[i - 1] = np.abs(w_turn[i - 1])
# Frontier size
w_size[i - 1] = group_length
# Save frontier groupings as an image (for debugging purposes)
cmap = plt.cm.jet
norm = plt.Normalize(vmin=labeled_frontiers.min(),
vmax=labeled_frontiers.max())
image = cmap(norm(labeled_frontiers))
plt.imsave('frontiers.png', np.rot90(image))
# Remove invalid goals and weights
valids = w_dist != -1
goals = goals[valids]
w_dist = w_dist[valids]
w_turn = w_turn[valids]
w_size = w_size[valids]
w_obst = w_obst[valids]
# 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_size = (w_size - min(w_size)) / (max(w_size) - min(w_size))
# Cancel turn weight for frontiers that have obstacles
w_turn[np.where(w_obst != 0)] = 1
# Goal cost function
c_dist = 3
c_turn = 2
c_size = 1
c_obst = 4
costs = c_dist * w_dist + c_turn * w_turn + c_size * w_size + c_obst * w_obst
min_idx = costs.argmin()
Print.art_print("Target selection time: " + str(time.time() - tinit),
Print.ORANGE)
print costs
print goals
## Safety Distance from obstacles
# Goal Coordinates
grid_size = 20
[found_obstacle, closest_obstacle,
min_dist] = self.detectObstacles(grid_size, ogm, goals[min_idx])
if found_obstacle == False:
return [False, goals[min_idx]]
# Calculate new goal:
dist_from_obstacles = 10
normal_vector = goals[min_idx] - closest_obstacle
normal_vector = normal_vector / np.hypot(normal_vector[0],
normal_vector[1])
new_goal = closest_obstacle + dist_from_obstacles * normal_vector
# Check new goal for nearby obstacles
[found_obstacle, closest_obstacle, min_dist_new] = \
self.detectObstacles(grid_size, ogm, new_goal.round())
# Return
if min_dist < 7:
# return the target with max min_dist
if min_dist_new > min_dist:
return [True, new_goal.round(), goals[min_idx]]
else:
return [False, goals[min_idx]]
else:
return [False, goals[min_idx]]
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 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
)
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 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
)
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
)
def readMap(self, data):
# OGM is a 2D array of size width x height
# The values are from 0 to 100
# 0 is an unoccupied pixel
# 100 is an occupied pixel
# 50 or -1 is the unknown
# Locking the map
self.map_compute = True
self.ros_ogm = data
# Reading the map pixels
self.ogm_info = data.info
if self.have_map == False or \
self.ogm_info.width != self.prev_ogm_info.width or \
self.ogm_info.height != self.prev_ogm_info.height:
self.ogm = numpy.zeros((data.info.width, data.info.height), \
dtype = numpy.int)
Print.art_print("Map & coverage expansion!", Print.GREEN)
self.prev_ogm_info = self.ogm_info
# Update coverage container as well
coverage_copy = numpy.zeros(
[self.ogm_info.width, self.ogm_info.height])
print "Coverage copy new size: " + str(coverage_copy.shape)
self.coverage_ogm.info = self.ogm_info
self.coverage_ogm.data = \
numpy.zeros(self.ogm_info.width * self.ogm_info.height)
if self.have_map == True:
print "Copying coverage field" + str(self.coverage.shape)
for i in range(0, self.coverage.shape[0]):
for j in range(0, self.coverage.shape[1]):
coverage_copy[i][j] = self.coverage[i][j]
# Coverage now gets the new size
self.coverage = numpy.zeros(
[self.ogm_info.width, self.ogm_info.height])
for i in range(0, self.coverage.shape[0]):
for j in range(0, self.coverage.shape[1]):
self.coverage[i][j] = coverage_copy[i][j]
print "New coverage info: " + str(self.coverage.shape)
for i in range(0, self.coverage.shape[0]):
for j in range(0, self.coverage.shape[1]):
index = int(i + self.ogm_info.width * j)
self.coverage_ogm.data[index] = self.coverage[i][j]
# TODO: Thats not quite right - the origins must be checked
for x in range(0, data.info.width):
for y in range(0, data.info.height):
self.ogm[x][y] = data.data[x + data.info.width * y]
# Get the map's resolution - each pixel's side in meters
self.resolution = data.info.resolution
# Get the map's origin
self.origin['x'] = data.info.origin.position.x
self.origin['y'] = data.info.origin.position.y
# Keep a copy
self.ogm_copy = numpy.copy(self.ogm)
# Update coverage
x = self.robot_pose['x_px']
y = self.robot_pose['y_px']
xx = int(self.robot_pose['x_px'] +
abs(self.origin['x'] / self.resolution))
yy = int(self.robot_pose['y_px'] +
abs(self.origin['y'] / self.resolution))
for i in range(-20, 20):
for j in range(-20, 20):
if self.ogm[xx + i, yy + j] > 49 or self.ogm[xx + i,
yy + j] == -1:
continue
self.coverage[xx + i, yy + j] = 100
index = int((xx + i) + self.ogm_info.width * (yy + j))
self.coverage_ogm.data[index] = 100
self.coverage_publisher.publish(self.coverage_ogm)
#NOTE: uncomment this to save coverage in image
#scipy.misc.imsave('~/Desktop/test.png', self.coverage)
# Unlock the map
self.map_compute = False
# If it is copied wait ...
while self.map_token == True:
pass
# This is for the navigation
if self.have_map == False:
self.have_map = True
Print.art_print("Robot perception: Map initialized", Print.GREEN)
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
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)
#print(ogm_limits)
# 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 statement that makes the target selection random in case the remaining nodes are two
if len(nodes) <= 2:
target = self.selectRandomTarget(ogm, coverage, brush, ogm_limits)
return target
pose_global_x = int(robot_pose['x_px'] - origin['x'] / resolution)
pose_global_y = int(robot_pose['y_px'] - origin['y'] / resolution)
#the folowing variables receive the values read by the sonar sensor
sonar_left = self.sonar.sonar_left_range
sonar_right = self.sonar.sonar_right_range
sonar_front = self.sonar.sonar_front_range
#a total sum is calculated for the front,right and left sonar sensors
sonar_sum = sonar_left + sonar_right + sonar_front
numrows = ogm.shape[1]
numcols = ogm.shape[0]
#if statement used in case the robot is in a tight spot and determines the target in the direction there is maximum space
if sonar_sum < 1.2:
target = self.sonar_avoidance(pose_global_x, pose_global_y, ogm,
numcols, numrows)
return target
#in case of a time out or a failure in path planning
if self.method == 'random' or force_random == True:
target = [
self.node2_index_x, self.node2_index_y
] #sets the current target as the previously calculated second best-scored target
if self.timeout_happened == 1:
target = self.sonar_avoidance(pose_global_x, pose_global_y,
ogm, numcols, numrows)
self.timeout_happened = 0
return target
self.timeout_happened = 1
return target
########################################################################
sum_min = 10000000
dist_min = 10000000
node_index_x = 0 #x value of the node with the lowest total cost
node_index_y = 0 #y value of the node with the lowest total cost
topo_cost = [] #list of topological costs for each node
dist_cost = [] #list of distance costs for each node
cov_cost = [] #list of coverage costs for each node
#using the brushfire array in order to calculate topology cost for each node
for n in nodes:
sum_temp = 0
index = n[1] + 1
while brush[n[0], index] != 0:
sum_temp += 1
index += 1
if index == numrows - 1: #numrows
break
index = n[1] - 1
while brush[n[0], index] != 0:
sum_temp += 1
index -= 1
if index == 0:
break
index = n[0] + 1
while brush[index, n[1]] != 0:
sum_temp += 1
index += 1
if index == numcols - 1: #numcols
break
index = n[0] - 1
while brush[index, n[1]] != 0:
sum_temp += 1
index -= 1
if index == 0:
break
topo_cost.append(sum_temp / 4)
#using the coverage array in order to calculate coverage cost for each node
numrows = len(coverage)
numcols = len(coverage[0])
for n in nodes:
total_sum = 0
sum_temp = 0
index = n[1] + 1
if index >= numrows or n[0] >= numcols:
total_sum = 5000
cov_cost.append(total_sum)
continue
while coverage[n[0], index] != 100:
sum_temp += 1
index += 1
if index >= numcols - 1: #numrows-1:
break
total_sum += sum_temp * coverage[n[0], index] / 100
index = n[1] - 1
sum_temp = 0
while coverage[n[0], index] != 100:
sum_temp += 1
index -= 1
if index == 0:
break
total_sum += sum_temp * coverage[n[0], index] / 100
index = n[0] + 1
sum_temp = 0
if index >= numcols or n[1] >= numrows:
total_sum = 5000
cov_cost.append(total_sum)
continue
while coverage[index, n[1]] != 100:
sum_temp += 1
index += 1
if index >= numrows - 1: #numcols-1
break
total_sum += sum_temp * coverage[index, n[1]] / 100
index = n[0] - 1
sum_temp = 0
while coverage[index, n[1]] != 100:
sum_temp += 1
index -= 1
if index == 0:
break
total_sum += sum_temp * coverage[index, n[1]] / 100
if total_sum == 0:
total_sum = 5000
cov_cost.append(total_sum)
pose_global_x = int(robot_pose['x_px'] - origin['x'] / resolution)
pose_global_y = int(robot_pose['y_px'] - origin['y'] / resolution)
#eucledean distance between the robot pose and each node
dist = math.sqrt(
math.pow(pose_global_x - n[0], 2) +
math.pow(pose_global_y - n[1], 2))
dist_cost.append(dist)
maxi = 0
for i in range(0, len(cov_cost)):
if cov_cost[i] != 5000 and cov_cost[i] > maxi:
maxi = cov_cost[i]
for i in range(0, len(cov_cost)):
if cov_cost[i] == 5000:
cov_cost[i] = maxi * 1.2
#lists to store the normalized costs for each node
topo_cost_norm = []
dist_cost_norm = []
cov_cost_norm = []
final_cost = []
min_final_cost = 1000000
for i in range(0, len(dist_cost)):
topo_cost_norm.append((np.float(topo_cost[i] - min(topo_cost)) /
np.float(max(topo_cost) - min(topo_cost))))
dist_cost_norm.append((dist_cost[i] - min(dist_cost)) /
(max(dist_cost) - min(dist_cost)))
if np.float(max(cov_cost) - min(cov_cost)) != 0:
cov_cost_norm.append((np.float(cov_cost[i] - min(cov_cost)) /
np.float(max(cov_cost) - min(cov_cost))))
if max(cov_cost) != min(cov_cost):
final_cost.append(
4 * topo_cost_norm[i] + 2 * dist_cost_norm[i] +
4 * cov_cost_norm[i]
) #optimal factor values in order to determine the best node to approach
else:
final_cost.append(
6 * topo_cost_norm[i] + 4 * dist_cost_norm[i]
) # exception if statement for the case coverage array cannot be used yet
if (final_cost[i] < min_final_cost):
min_final_cost = final_cost[i]
self.node2_index_x = node_index_x #storing the second best node to approach in case of path planning failure or time out
self.node2_index_y = node_index_y #
node_index_x = nodes[i][0]
node_index_y = nodes[i][1]
target = [node_index_x, node_index_y]
#in case current target is the same with the previous , sonar avoidance function is used to modify the robot pose
if target == self.previous_target:
target = self.sonar_avoidance(pose_global_x, pose_global_y, ogm,
numcols, numrows)
self.previous_target = target
return target
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 __init__(self):
# Flags for debugging and synchronization
self.print_robot_pose = False
self.have_map = False
self.map_token = False
self.map_compute = False
# Holds the occupancy grid map
self.ogm = 0
self.ros_ogm = 0
self.ogm_copy = 0
# Holds the ogm info for copying reasons -- do not change
self.ogm_info = 0
self.prev_ogm_info = 0
# Holds the robot's total path
self.robot_trajectory = []
self.previous_trajectory_length = 0
# Holds the coverage information. This has the same size as the ogm
# If a cell has the value of 0 it is uncovered
# In the opposite case the cell's value will be 100
self.coverage = []
# Holds the resolution of the occupancy grid map
self.resolution = 0.05
# Origin is the translation between the (0,0) of the robot pose and the
# (0,0) of the map
self.origin = {}
self.origin['x'] = 0
self.origin['y'] = 0
# Initialization of robot pose
# x,y are in meters
# x_px, y_px are in pixels
self.robot_pose = {}
self.robot_pose['x'] = 0
self.robot_pose['y'] = 0
self.robot_pose['th'] = 0
self.robot_pose['x_px'] = 0
self.robot_pose['y_px'] = 0
self.coverage_ogm = OccupancyGrid()
self.coverage_ogm.header.frame_id = "map"
ogm_topic = rospy.get_param('ogm_topic')
robot_trajectory_topic = rospy.get_param('robot_trajectory_topic')
coverage_pub_topic = rospy.get_param('coverage_pub_topic')
self.map_frame = rospy.get_param('map_frame')
self.base_footprint_frame = rospy.get_param('base_footprint_frame')
# Use tf to read the robot pose
self.listener = tf.TransformListener()
# Read robot pose with a timer
rospy.Timer(rospy.Duration(0.11), self.readRobotPose)
# ROS Subscriber to the occupancy grid map
ogm_topic = rospy.get_param('ogm_topic')
rospy.Subscriber(ogm_topic, OccupancyGrid, self.readMap)
# Publisher of the robot trajectory
robot_trajectory_topic = rospy.get_param('robot_trajectory_topic')
self.robot_trajectory_publisher = rospy.Publisher(robot_trajectory_topic,\
Path, queue_size = 10)
# Publisher of the coverage field
coverage_pub_topic = rospy.get_param('coverage_pub_topic')
self.coverage_publisher = rospy.Publisher(coverage_pub_topic, \
OccupancyGrid, queue_size = 10)
# Read Cell size
self.cell_size = rospy.get_param('cell_size')
self.cell_matrix = numpy.zeros((1, 1))
self.current_cell = []
Print.art_print("Robot perception initialized", Print.GREEN)
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 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 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
)
