# load data
with open(os.path.dirname(os.path.abspath(__file__)) + '/data.txt', 'rb') as f:
positions = pickle.load(f)
# set initial joint position
robot.reset_joint_states(q=positions[0])
# draw a sphere at the position of the end-effector
sphere = world.load_visual_sphere(
position=robot.get_link_world_positions(link_id),
radius=0.05,
color=(1, 0, 0, 0.5))
sphere = Body(sim, body_id=sphere)
# perform simulation
for t in count():
# if no more joint positions, get out of the loop
if t >= len(positions):
break
# set joint positions
robot.set_joint_positions(positions[t], joint_ids=joint_ids)
# make the sphere follow the end effector
sphere.position = robot.get_link_world_positions(link_id)
# step in simulation
world.step(sleep_dt=1. / 240)
sphere.position = np.array([
0.5, r * np.cos(w * t + np.pi / 2),
(1. - r) + r * np.sin(w * t + np.pi / 2)
])
# get current end-effector position and velocity in the task/operational space
x = robot.get_link_world_positions(link_id)
dx = robot.get_link_world_linear_velocities(link_id)
# Get joint positions
q = robot.get_joint_positions()
# Get linear jacobian
if robot.has_floating_base():
J = robot.get_linear_jacobian(link_id, q=q)[:, qIdx + 6]
else:
J = robot.get_linear_jacobian(link_id, q=q)[:, qIdx]
# Pseudo-inverse: \hat{J} = J^T (JJ^T + k^2 I)^{-1}
Jp = robot.get_damped_least_squares_inverse(J, damping)
# evaluate damped-least-squares IK
dq = Jp.dot(kp * (sphere.position - x) - kd * dx)
# set joint positions
q = q[qIdx] + dq * dt
robot.set_joint_positions(q, joint_ids=joint_ids)
# step in simulation
world.step(sleep_dt=dt)
# get center of mass jacobian
Jcom = robot.get_center_of_mass_jacobian(qt)
print("CoM velocity: {}".format(robot.get_center_of_mass_velocity()))
print("Jcom.dot(qt) = {}".format(Jcom.dot(robot.get_joint_velocities())))
# Get Inertia Matrix
# M = robot.get_mass_matrix(q=qt)
# print("M: {}".format(M))
# Tracking of CoM velocity manipulability
velocity_manip = robot.compute_velocity_manipulability_ellipsoid(Jcom)
print("Mv: {}".format(velocity_manip[0:3, 0:3]))
# Plot current manipulability ellipsoid
if i % 10 == 0:
ellipsoid_id = robot.update_manipulability_ellipsoid(
link_id=-1,
ellipsoid_id=ellipsoid_id,
ellipsoid=10 * velocity_manip[:3, :3],
color=(0.75, 0.1, 0.1, 0.6))
# Obtaining joint velocity command
dq = robot.calculate_inverse_differential_kinematics_velocity_manipulability(
Jcom, desired_velocity_manip, Km)[0]
# Set joint position
q = qt + dq * dt
robot.set_joint_positions(q)
world.step(sleep_dt=dt)
class Robot:
def __init__(self, sim, world):
self.robot = KukaIIWA(sim)
self.world = world
# change the robot visual
self.robot.change_transparency()
self.robot.draw_link_frames(link_ids=[-1, 0])
self.start_pos = np.array([-0.75, 0., 0.75])
self.end_pos = np.array([0.75, 0., 0.75])
self.pid = {'kp': 100, 'kd': 0}
self.ee_id = self.robot.get_end_effector_ids(end_effector=0)
def send_robot_to(self, location):
# define useful variables for IK
dt = 1. / 240
link_id = self.robot.get_end_effector_ids(end_effector=0)
joint_ids = self.robot.joints # actuated joint
# joint_ids = joint_ids[2:]
damping = 0.01 # for damped-least-squares IK
qIdx = self.robot.get_q_indices(joint_ids)
# for t in count():
# get current position in the task/operational space
x = self.robot.sim.get_link_world_positions(body_id=self.robot.id,
link_ids=link_id)
dx = self.robot.get_link_world_linear_velocities(link_id)
# Get joint configuration
q = self.robot.sim.get_joint_positions(self.robot.id,
self.robot.joints)
# Get linear jacobian
if self.robot.has_floating_base():
J = self.robot.get_linear_jacobian(link_id, q=q)[:, qIdx + 6]
else:
J = self.robot.get_linear_jacobian(link_id, q=q)[:, qIdx]
# Pseudo-inverse
Jp = self.robot.get_damped_least_squares_inverse(J, damping)
# evaluate damped-least-squares IK
dq = Jp.dot(self.pid['kp'] * (location - x) - self.pid['kd'] * dx)
# set joint velocities
# robot.set_joint_velocities(dq)
# set joint positions
q = q[qIdx] + dq * dt
self.robot.set_joint_positions(q, joint_ids=joint_ids)
return x
def go_home(self):
for t in count():
x = self.send_robot_to(self.start_pos)
# step in simulation
self.world.step()
# Check if robot has reached the required position
error = np.linalg.norm(self.start_pos - x)
if error < 0.01 or t > 500:
break
def go_to_end(self):
self.send_robot_to(self.end_pos)
class Environment:
def __init__(self, spheres=[[0.09, -0.44, 0.715, 1]], cuboids=None):
# Define size of the map
self.res = 1.
# Initialize XBox controller
self.controller = XboxControllerInterface(use_thread=True,
verbose=False)
# Create simulator
self.sim = Bullet()
# create world
self.world = BasicWorld(self.sim)
# create robot
self.robot = KukaIIWA(self.sim)
# define start/end pt
self.start_pt = [-0.75, 0., 0.75]
self.end_pt = [0.75, 0., 0.75]
# Initialize robot location
self.send_robot_to(np.array(self.start_pt), dt=2)
# Get via points from user
via_pts = self.get_via_pts(
) # [[-0.19, -0.65, 0.77], [0.51, -0.44, 0.58]]
self.motion_targets = [self.start_pt] + via_pts + [self.end_pt]
def send_robot_to(self, location, dt):
start = time.perf_counter()
for _ in count():
joint_ids = self.robot.joints
link_id = self.robot.get_end_effector_ids(end_effector=0)
qIdx = self.robot.get_q_indices(joint_ids)
x = self.robot.sim.get_link_world_positions(body_id=self.robot.id,
link_ids=link_id)
q = self.robot.calculate_inverse_kinematics(link_id,
position=location)
self.robot.set_joint_positions(q[qIdx], joint_ids)
self.world.step()
if time.perf_counter() - start >= dt:
break
def get_via_pts(self):
print(
"Move green dot to via point. Press 'A' to save. Press 'X' to finish."
)
X_desired = [0., 0., 1.3]
speed_scale_factor = 0.01
via_pts = []
sphere = None
for _ in count():
last_trigger = self.controller.last_updated_button
movement = self.controller[last_trigger]
if last_trigger == 'LJ':
X_desired[0] = X_desired[0] + round(movement[0],
1) * speed_scale_factor
X_desired[1] = X_desired[1] + round(movement[1],
1) * speed_scale_factor
elif last_trigger == 'RJ':
X_desired[2] = X_desired[2] + round(movement[1],
1) * speed_scale_factor
elif last_trigger == 'A' and movement == 1:
print("Via point added")
via_pts.append(X_desired)
time.sleep(0.5)
elif last_trigger == 'X' and movement == 1:
self.world.sim.remove_body(sphere)
print("Via point generation complete.")
break
if sphere:
self.world.sim.remove_body(sphere)
sphere = self.world.load_visual_sphere(X_desired,
radius=0.01,
color=(0, 1, 0, 1))
return via_pts
# IK using pybullet #
#####################
# OPTION 1: using `calculate_inverse_kinematics`###
if solver_flag == 0:
for _ in count():
# # get current position in the task/operational space
# x = robot.get_link_positions(link_id, wrt_link_id)
x = robot.get_link_world_positions(link_id)
# print("(xd - x) = {}".format(xd - x))
# perform full IK
q = robot.calculate_inverse_kinematics(link_id, position=xd)
# set the joint positions
robot.set_joint_positions(q[qIdx], joint_ids)
# step in simulation
world.step(sleep_dt=dt)
# OPTION 2: using Jacobian and manual damped-least-squares IK ###
elif solver_flag == 1:
kp = 50 # 5 if velocity control, 50 if position control
kd = 0 # 2*np.sqrt(kp)
for _ in count():
# get current position in the task/operational space
# x = robot.get_link_positions(link_id, wrt_link_id)
# dx = robot.get_link_linear_velocities(link_id, wrt_link_id)
x = robot.get_link_world_positions(link_id)
dx = robot.get_link_world_linear_velocities(link_id)