def __init__(self,
pose=TransformStamped(
header=Header(frame_id='panda_link8'),
child_frame_id='target',
transform=Transform(rotation=Quaternion(
*tf.quaternion_about_axis(numpy.pi / 4, [0, 0, 1])),
translation=Vector3(0, 0, 0.105)))):
self.robot = RobotModel()
self.robot._add(Joint(pose)) # add a fixed end-effector transform
self.pub = rospy.Publisher('/target_joint_states',
JointState,
queue_size=10)
self.joint_msg = JointState()
self.joint_msg.name = [j.name for j in self.robot.active_joints]
self.joint_msg.position = numpy.asarray([
(j.min + j.max) / 2 + 0.1 * (j.max - j.min) * random.uniform(0, 1)
for j in self.robot.active_joints
])
self.target_link = pose.child_frame_id
self.T, self.J = self.robot.fk(
self.target_link,
dict(zip(self.joint_msg.name, self.joint_msg.position)))
self.im_server = MyInteractiveMarkerServer("controller", self.T)
def __init__(self, hip_height, torso_angle, lift_off_slope, step_height,
step_length, put_down_slope):
self.hip_height = hip_height
self.torso_angle = torso_angle
self.lift_off_slope = lift_off_slope
self.step_height = step_height
self.step_length = step_length
self.put_down_slope = put_down_slope
self.upper_limp_part = UpperLimbWhileWalking(torso_angle, hip_height)
self.hip_offset = self.upper_limp_part.hip_offset
self.painter = Painter()
self.robot_model = RobotModel(self.painter)
def draw_robot_with_center_of_mass(robot_name=None, read_state_string=None):
assert robot_name is not None or read_state_string is not None
if read_state_string is None:
read_state_string = RealRobotClient().read_state()
assert 'nan' not in read_state_string
angles = ReadStateParser(read_state_string).get_all_joint_radians()
painter = Painter(azimuth=0, elevation=0)
robot_model = RobotModel(painter)
center_of_mass = compute_center_of_mass_from_read_state_string(angles, painter, robot_model,\
draw_robot=True, draw_center_of_mass=True)
# robot_model.draw_coordinates() # draw coordinates lastly because we want them on top layer
robot_model.show()
def forward_kinematics(robot_name=None, read_state_string=None):
assert robot_name is not None or read_state_string is not None
if read_state_string is None:
read_state_string = RealRobotClient().read_state()
assert 'nan' not in read_state_string
r = RobotModel(Painter())
angles = ReadStateParser(read_state_string).get_all_joint_radians()
r.draw_neck_and_head(neck_transversal_radians=angles['neck_transversal'],
neck_lateral_radians=angles['neck_lateral'])
r.draw_left_arm(
left_shoulder_lateral_radians=angles['left_shoulder_lateral'],
left_shoulder_frontal_radians=angles['left_shoulder_frontal'],
left_elbow_lateral_radians=angles['left_elbow_lateral'])
r.draw_right_arm(
right_shoulder_lateral_radians=angles['right_shoulder_lateral'],
right_shoulder_frontal_radians=angles['right_shoulder_frontal'],
right_elbow_lateral_radians=angles['right_elbow_lateral'])
r.draw_left_lower_limp(
left_hip_transversal_radians=angles['left_hip_transversal'],
left_hip_frontal_radians=angles['left_hip_frontal'],
left_hip_lateral_radians=angles['left_hip_lateral'],
left_knee_lateral_radians=angles['left_knee_lateral'],
left_ankle_lateral_radians=angles['left_ankle_lateral'],
left_ankle_frontal_radians=angles['left_ankle_frontal'])
r.draw_right_lower_limp(
right_hip_transversal_radians=angles['right_hip_transversal'],
right_hip_frontal_radians=angles['right_hip_frontal'],
right_hip_lateral_radians=angles['right_hip_lateral'],
right_knee_lateral_radians=angles['right_knee_lateral'],
right_ankle_lateral_radians=angles['right_ankle_lateral'],
right_ankle_frontal_radians=angles['right_ankle_frontal'])
r.draw_torso()
r.draw_coordinates(
) # draw coordinates lastly because we want them on top layer
r.show()
from util.painter import Painter
from robot_model import RobotModel
from joint.joint_angle import *
r = RobotModel(Painter())
r.draw_neck_and_head()
r.draw_left_arm()
# r.draw_left_arm(radians(-30), radians(60), radians(-30))
# r._painter.draw_point(r.draw_left_arm(radians(0), radians(-60), radians(0), color='b-')[-1])
r.draw_right_arm()
# r.draw_right_arm(radians(0), radians(30), radians(160), color='b-')
# r._painter.draw_point(r.draw_right_arm(radians(0), radians(60), radians(0), color='b-')[-1])
# r.draw_left_lower_limp()
# r.draw_right_lower_limp(right_knee_lateral_radians=radians(150))
r.draw_left_lower_limp(
# LeftHipTransversal().outward(0).radians,
radians(-0),
LeftHipFrontal().outward(0).angle,
LeftHipLateral().forward(0).radians,
LeftKneeLateral().backward(0).radians,
# radians(150),
LeftAnkleLateral().forward(0).radians,
LeftAnkleFrontal().outward(-0).radians)
r.draw_right_lower_limp(
RightHipTransversal().inward(0).radians,
# radians(-0),
RightHipFrontal().outward(0).angle,
length * math.cos(yaw),
length * math.sin(yaw),
head_length=width,
head_width=width)
plt.plot(x, y)
from robot_model import RobotModel
if __name__ == '__main__':
gx = 10.0
gy = 12.0
print(__file__ + " start!!")
# initial state [x(m), y(m), yaw(rad), v(m/s), omega(rad/s)]
# x = np.array([0.0, 0.0, math.pi / 8.0, 0.0, 0.0])
motionModel = RobotModel(2, 0, math.pi / 3 * 2, 0.7, math.pi / 3 * 5, 0.0,
0.0, math.pi / 8.0, 0.0, 0.0)
x = motionModel.state
# goal position [x(m), y(m)]
target_model = RobotModel(2, 0, math.pi / 3 * 2, 0.7, math.pi / 3 * 5, gx,
gy, -math.pi / 2.0, 0.0, 0.0)
goal = target_model.state[:2]
# obstacles [x(m) y(m), ....]
ob = np.array([[-1, -1], [0, 2], [4.0, 2.0], [5.0, 4.0], [5.0, 5.0],
[5.0, 6.0], [5.0, 9.0], [8.0, 9.0], [7.0, 9.0],
[12.0, 12.0], [4.0, 12.0], [3.5, 15.8], [12.1, 17.0],
[7.16, 14.6], [8.6, 13.0]])
u = np.array([0.0, 0.0])
config = ConfigDWA(2, 0, math.pi / 3 * 2, 0.7, math.pi / 3 * 5)
traj = np.array(x)
import time
from math import *
import numpy as np
from a_star_planner import AStarPlanner
from robot_model import RobotModel
import matplotlib.image as mpimg
from grid_map import GridMap
rospy.init_node('test_a_star_planner', anonymous=True)
vehicle_config = yaml.load(
open('/home/kai/catkin_ws/src/drproj/vehicle_config.yaml'))
half_length = 0.5 * vehicle_config['length']
half_width = 0.5 * vehicle_config['width']
radius = sqrt(half_length**2 + half_width**2)
robot_model = RobotModel(radius)
global_planner = AStarPlanner(robot_model)
free = mpimg.imread('/home/kai/catkin_ws/src/drproj/free.png')
global_map_config = yaml.load(open('/home/kai/catkin_ws/src/drproj/free.yaml'))
global_map = GridMap(free, global_map_config['resolution'])
global_map.show('rviz_global_grid_map')
global_map.print()
global_mission = yaml.load(
open('/home/kai/catkin_ws/src/drproj/global_mission.yaml'))
start_grid_x = int(global_mission['start'][0] / global_map.resolution + 0.5)
start_grid_y = int(global_mission['start'][1] / global_map.resolution + 0.5)
goal_grid_x = int(global_mission['goal'][0] / global_map.resolution + 0.5)
goal_grid_y = int(global_mission['goal'][1] / global_map.resolution + 0.5)
start = (start_grid_x, start_grid_y)
import matplotlib.image as mpimg
from grid_map import GridMap
from robot_model import RobotModel
dr = mpimg.imread('/home/kai/Pictures/dr.png')
global_grid_map = GridMap(dr, 1.0)
global_grid_map.print()
robot_model = RobotModel(2.0)
controls = robot_model.get_controls((639, 200), global_grid_map)
print(controls)
def setUp(self):
self.painter = Painter()
self.r = RobotModel(Painter())
from util.painter import Painter
from util.point import Point
from robot_model import RobotModel
from robot_spec.position_data import *
from inverse_kinematics.arm import InverseKinematicsArm
from inverse_kinematics.lower_limp import InverseKinematicsLeg
from inverse_kinematics.head import move_head
from robot_client_server.robot_clients import RealRobotClient
from joint import *
painter = Painter(azimuth=0, elevation=50)
r = RobotModel(painter)
def inverse_kinematics(head_target_position, l_arm_target_position,
r_arm_target_position, l_leg_target_position,
l_hip_transversal_radians, r_leg_target_position,
r_hip_transversal_radians, do_actuate):
# move kinematic chains
neck_transversal_radians, neck_lateral_radians\
= move_head(head_target_position, painter)
l_shoulder_lateral_radians, l_shoulder_frontal_radians, l_elbow_lateral_radians\
= InverseKinematicsArm(l_arm_target_position, r, painter, is_left=True).get_angles()
r_shoulder_lateral_radians, r_shoulder_frontal_radians, r_elbow_lateral_radians\
= InverseKinematicsArm(r_arm_target_position, r, painter, is_left=False).get_angles()
l_hip_frontal_radians, l_hip_lateral_radians, l_knee_lateral_radians, l_ankle_lateral_radians, l_ankle_frontal_radians\
= InverseKinematicsLeg(is_left=True, l_hip_transversal_radians, l_leg_target_position, r, painter).get_angles()
r_hip_frontal_radians, r_hip_lateral_radians, r_knee_lateral_radians, r_ankle_lateral_radians, r_ankle_frontal_radians\
= InverseKinematicsLeg(is_left=False, r_hip_transversal_radians, r_leg_target_position, r, painter).get_angles()
def __init__(self):
self.max_v = MAX_V
self.min_v = MIN_V
self.max_w = MAX_W
self.max_acc_v = 0.7
self.max_acc_w = pi / 3.0 * 5
self.init_x = 0.0
self.init_y = 6.0
self.init_yaw = pi / 8.0
self.robot_radius = 0.3
self.ob_radius = 0.3
self.target_radius = 0.3
self.dt = 0.1
self.target_init_x = 10
self.target_init_y = 4
self.target_init_yaw = pi / 2.0
self.target_u = None
self.pursuitor_model = RobotModel(self.max_v, self.min_v, self.max_w,
self.max_acc_v, self.max_acc_w,
self.init_x, self.init_y,
self.init_yaw)
self.evader_model = RobotModel(self.max_v, self.min_v, self.max_w,
self.max_acc_v, self.max_acc_w,
self.target_init_x, self.target_init_y,
self.target_init_yaw)
self.config_dwa = ConfigDWA(self.max_v,
self.min_v,
self.max_w,
self.max_acc_v,
self.max_acc_w,
robot_raduis=self.robot_radius,
obstacle_radius=self.ob_radius,
dt=self.dt)
self.confg_apf = ConfigAPF(self.max_v,
self.min_v,
self.max_w,
self.max_acc_v,
self.max_acc_w,
obstacle_radius=self.ob_radius)
self.confg_pn = ConfigPN()
"""env parameters need reset
"""
self.ob_list = None
# self.ob_list = None
self.last_obs = None
self.last_pur_state = None
self.last_tar_state = None
# trajectory
self.traj = np.array(self.get_state())
# state done
self.collision = False
self.catch = False
# actions
# self.v_reso = 0.1
# self.w_reso = pi/18.0
#
#
# self.action_table = []
# for v in np.arange(self.min_v, self.max_v+self.v_reso, self.v_reso):
# for w in np.arange(-self.max_w, self.max_w+self.w_reso, self.w_reso):
# self.action_table.append(np.array([v, w]))
# self.action_num = len(self.action_table)
self.action_num = 2
self.action_space = spaces.Box(np.array([-1]), np.array(
[+1])) # v accelerate, w accelerate
# observations = laser, goal_angle, goal_distance
self.percept_region = np.array([-4, 4, -2,
6]) # left, right, behind, ahead
self.basic_sample_reso = 0.1 # sampling resolution of percept region
self.sample_reso_scale = 1.5 # sampling density increases with distance from
def cal_samples(sample_length, basic_sample_reso, sample_reso_scale):
# get sample number
# x = sympy.Symbol('x')
# result_dict = sympy.solve(
# [basic_sample_reso * (1 - sample_reso_scale ** x) / (1 - sample_reso_scale)
# - abs(sample_length)], [x])
# print(result_dict)
a = math.log(sample_length / basic_sample_reso *
(sample_reso_scale - 1) + 1)
b = math.log(sample_reso_scale)
# print(str(a)+", "+str(b))
x = a / b
return int(x)
left_samples = right_samples = cal_samples(abs(self.percept_region[0]),
self.basic_sample_reso,
self.sample_reso_scale)
behind_samples = cal_samples(abs(self.percept_region[2]),
self.basic_sample_reso,
self.sample_reso_scale)
ahead_samples = cal_samples(abs(self.percept_region[3]),
self.basic_sample_reso,
self.sample_reso_scale)
self.sample_map_center = np.array(
[left_samples - 1, behind_samples - 1])
self.sample_map_width = left_samples + right_samples - 1
self.sample_map_height = behind_samples + ahead_samples - 1
self.percept_sample_map = []
for i in range(self.sample_map_width):
tmp_list_y = []
for j in range(self.sample_map_height):
x = self.basic_sample_reso * (1 - self.sample_reso_scale ** abs(i - self.sample_map_center[0])) \
/ (1 - self.sample_reso_scale)
x = x if i - self.sample_map_center[0] >= 0 else -x
y = self.basic_sample_reso * (1 - self.sample_reso_scale ** abs(j - self.sample_map_center[1])) \
/ (1 - self.sample_reso_scale)
y = y if j - self.sample_map_center[1] >= 0 else -y
tmp_list_y.append(np.array([x, y]))
self.percept_sample_map.append(tmp_list_y)
# self.obs_num = self.pursuitor_model.laser_num + 2
# high = np.hstack((np.zeros(self.pursuitor_model.laser_num)+self.pursuitor_model.laser_max_range, pi, 50))
# low = np.hstack((np.zeros(self.pursuitor_model.laser_num), -pi, 0))
# self.observation_space = spaces.Box(low, high, dtype=np.float)
self.obs_num = 2 + 2
self.observation_space = spaces.Box(-np.inf,
np.inf,
shape=(self.obs_num, ),
dtype=float)
# seed
self.np_random = None
self.seed()
# time limit
self.limit_step = 300
self.count = 0
self.action_plot = 0
# record
self.pursuit_strategy = None
self.traj_pursuitor = None
self.traj_evader = None
# intelligent evasion mode
self.evasion_mode = False
self.config_evasion = ConfigDWA(self.max_v * 0.9,
self.min_v,
self.max_w,
self.max_acc_v,
self.max_acc_w,
robot_raduis=self.robot_radius,
obstacle_radius=self.ob_radius,
dt=self.dt)
self.evasion_route = None
self.evasion_route_index = None
__author__ = 'zhao'
from inverse_kinematics.lower_limp import InverseKinematicsLeg
from util.painter import Painter
from util.point import Point
from robot_model import RobotModel
is_left = False
# target_position = Point((-8.0, 7.25, -44.699999999999989))
target_position = Point((-8.0, 7.25, -44.7))
painter = Painter()
robot_model = RobotModel(painter)
r_hip_transversal_radians = 0
r_hip_frontal_radians, r_hip_lateral_radians, r_knee_lateral_radians, r_ankle_lateral_radians, r_ankle_frontal_radians\
= InverseKinematicsLeg(is_left=is_left, hip_transversal_radians=r_hip_transversal_radians, target_position=target_position, robot_model=robot_model, painter=painter).get_angles()
# r_hip_frontal_radians, r_hip_lateral_radians, r_knee_lateral_radians, r_ankle_lateral_radians, r_ankle_frontal_radians = [0] * 5
# painter.draw_point(target_position)
robot_model.draw_neck_and_head()
robot_model.draw_torso()
robot_model.draw_left_arm()
robot_model.draw_right_arm()
robot_model.draw_left_lower_limp()
robot_model.draw_right_lower_limp(
right_hip_transversal_radians=r_hip_transversal_radians,
right_hip_frontal_radians=r_hip_frontal_radians,
right_hip_lateral_radians=r_hip_lateral_radians,
right_knee_lateral_radians=r_knee_lateral_radians,
right_ankle_lateral_radians=r_ankle_lateral_radians,
right_ankle_frontal_radians=r_ankle_frontal_radians)
def __init__(self):
self.max_v = 2
self.min_v = -0.3
self.max_w = pi / 3.0 * 2
self.max_acc_v = 0.7
self.max_acc_w = pi / 3.0 * 5
self.init_x = 0.0
self.init_y = 6.0
self.init_yaw = pi / 8.0
self.robot_radius = 0.3
self.ob_radius = 0.3
self.target_radius = 0.3
self.dt = 0.1
self.target_init_x = 10
self.target_init_y = 4
self.target_init_yaw = pi / 2.0
self.target_u = None
self.pursuitor_model = RobotModel(self.max_v, self.min_v, self.max_w,
self.max_acc_v, self.max_acc_w,
self.init_x, self.init_y,
self.init_yaw)
self.evader_model = RobotModel(self.max_v, self.min_v, self.max_w,
self.max_acc_v, self.max_acc_w,
self.target_init_x, self.target_init_y,
self.target_init_yaw)
self.config_dwa = ConfigDWA(self.max_v,
self.min_v,
self.max_w,
self.max_acc_v,
self.max_acc_w,
robot_raduis=self.robot_radius,
obstacle_radius=self.ob_radius,
dt=self.dt)
self.confg_apf = ConfigAPF(self.max_v,
self.min_v,
self.max_w,
self.max_acc_v,
self.max_acc_w,
obstacle_radius=self.ob_radius)
self.confg_pn = ConfigPN()
"""env parameters need reset
"""
self.ob_list = np.array([[0, 2], [4.0, 2.0], [5.0, 4.0], [5.0, 6.0],
[7.0, 9.0], [12.0, 12.0]])
self.last_obs = None
self.last_pur_state = None
self.last_tar_state = None
# trajectory
self.traj = np.array(self.get_state())
# state done
self.collision = False
self.catch = False
# actions
# self.v_reso = 0.1
# self.w_reso = pi/18.0
#
#
# self.action_table = []
# for v in np.arange(self.min_v, self.max_v+self.v_reso, self.v_reso):
# for w in np.arange(-self.max_w, self.max_w+self.w_reso, self.w_reso):
# self.action_table.append(np.array([v, w]))
# self.action_num = len(self.action_table)
self.action_num = 2
self.action_space = spaces.Discrete(self.action_num)
# observations = image
self.screen_height = 600 # pixel
self.screen_width = 600 # pixel
self.state_height = 150 # pixel
self.state_width = 150 # pixel
self.world_width = 20.0 # m
self.observation_space = spaces.Box(low=0,
high=255,
shape=(self.state_height,
self.state_width, 3),
dtype=np.uint8)
# seed
self.np_random = None
self.seed()
# time limit
self.limit_step = 300
self.count = 0
self.action_plot = 0
self.viewer = None
def main(gx=8, gy=2):
show_animation = True
print(__file__ + " start!!")
max_v = 2
min_v = 0
max_w = pi / 3 * 2
max_acc_v = 0.7
max_acc_w = pi / 3.0 * 5
init_x = 0.0
init_y = 6
init_yaw = pi / 8.0
robot_radius = 0.3
ob_radius = 0.3
goal_radius = 0.3
pursuitor_model = RobotModel(max_v, min_v, max_w, max_acc_v, max_acc_w,
init_x, init_y, init_yaw)
target_model = RobotModel(max_v, min_v, max_w, max_acc_v, max_acc_w, gx,
gy, -pi / 2.0)
# obstacles [x(m) y(m), ....]
ob = np.array([[0, 2], [4.0, 2.0], [5.0, 4.0], [5.0, 6.0], [7.0, 9.0]])
config = ConfigAPF(2, 0, math.pi / 3, 0.7, math.pi / 3)
traj = np.array(pursuitor_model.state)
traj_u = np.array([0, 0])
traj_target = np.array(target_model.state)
for i in range(2000):
logging.info("")
logging.info(i)
u = apf_control(config, pursuitor_model.state, target_model.state, ob)
traj_u = np.vstack((traj_u, u))
print(u)
u[1] = pursuitor_model.rot_to_angle(u[1])
# u[0]=0
x = pursuitor_model.motion(u, config.dt)
traj = np.vstack((traj, x)) # store state history
goal = target_model.motion([1.8, pi / 5], config.dt)
traj_target = np.vstack((traj_target, goal))
# print(traj)
if show_animation:
plt.cla()
plt.plot(x[0], x[1], "xr")
plt.plot(goal[0], goal[1], "xb")
plt.plot(ob[:, 0], ob[:, 1], "ok")
plot_arrow(x[0], x[1], x[2])
plt.axis("equal")
plt.grid(True)
plt.pause(0.0001)
# check goal
if math.sqrt((x[0] - goal[0])**2 +
(x[1] - goal[1])**2) <= (pursuitor_model.robot_radius +
target_model.robot_radius):
print("Goal!!")
break
# check collision
collision = False
for obstacle in ob:
if math.sqrt((x[0] - obstacle[0])**2 +
(x[1] - obstacle[1])**2) <= (
pursuitor_model.robot_radius +
config.obstacle_radius):
collision = True
if collision:
print("Collision!")
break
prefix = "apf_flaw/4_"
np.save(prefix + "traj.npy", traj)
np.save(prefix + "traj_target.npy", traj_target)
np.save(prefix + "traj_u.npy", traj_u)
print("Done")
if show_animation:
plt.plot(traj[:, 0], traj[:, 1], "-r")
plt.plot(traj_target[:, 0], traj_target[:, 1], "-g")
fig = plt.gcf()
# fig.set_size_inches(6,6)
plt.xlabel('X(m)')
plt.ylabel('Y(m)')
# plt.axis("auto")
plt.tight_layout()
print(plt.xlim((-1.488585497312541, 14.14373130420207)))
print(plt.ylim((0.4404926013282211, 12.089089959876205)))
plt.figure()
plt.plot(np.arange(0, len(traj)) * config.dt,
traj_u[:, 1],
label=r'Input $\theta$')
plt.plot(np.arange(0, len(traj)) * config.dt,
traj[:, 2],
label=r'Actual $\theta$')
plt.xlabel('T(s)')
plt.ylabel(r'$\theta$(rad)')
plt.legend()
plt.show()
max_acc_v = 0.7
max_acc_w = pi / 3.0 * 5
init_x = 0.0
init_y = 6.0
init_yaw = pi / 8.0
robot_radius = 0.3
ob_radius = 0.3
target_radius = 0.3
dt = 0.1
target_init_x = 10
target_init_y = 4
target_init_yaw = pi / 2.0
target_u = None
evader_model = RobotModel(max_v, min_v, max_w, max_acc_v, max_acc_w,
target_init_x, target_init_y, target_init_yaw)
ob_wall=[]
top, bottom, left, right = 20.0, -10.0, -10.0, 20.0
# top y=20
for x in np.arange(left, right, 2*ob_radius):
ob_wall.append(np.array([x, top]))
# bottom y=-10
for x in np.arange(left, right, 2*ob_radius):
ob_wall.append(np.array([x, bottom]))
# left
for y in np.arange(bottom+2*ob_radius, top, 2*ob_radius):
ob_wall.append(np.array([left, y]))
# right
for y in np.arange(bottom, top, 2*ob_radius):
ob_wall.append(np.array([right, y]))
