def test_solve_ik_via_jacobian(self):
arm = Panda()
waypoint = Dummy('Panda_waypoint')
new_config = arm.solve_ik_via_jacobian(
waypoint.get_position(), waypoint.get_orientation())
arm.set_joint_positions(new_config)
self.assertTrue(np.allclose(
arm.get_tip().get_pose(), waypoint.get_pose(), atol=0.001))
from os.path import dirname, join, abspath
from pyrep import PyRep
from pyrep.errors import IKError
from pyrep.robots.arms.panda import Panda
SCENE_FILE = join(dirname(abspath(__file__)), 'scene_panda_reach_target.ttt')
pr = PyRep()
pr.launch(SCENE_FILE, headless=False, responsive_ui=True)
pr.start()
agent = Panda()
starting_joint_positions = agent.get_joint_positions()
(x, y, z), q = agent.get_tip().get_position(), agent.get_tip().get_quaternion()
# Try solving via linearisation
new_joint_pos = agent.solve_ik_via_jacobian([x, y, z - 0.01], quaternion=q)
new_joint_pos = agent.solve_ik_via_jacobian([x, y, z - 0.05], quaternion=q)
new_joint_pos = agent.solve_ik_via_jacobian([x, y, z - 0.1], quaternion=q)
new_joint_pos = agent.solve_ik_via_jacobian([x, y, z - 0.2], quaternion=q)
# This will fail because the distance between start and goal is too far
try:
new_joint_pos = agent.solve_ik_via_jacobian([x, y, z - 0.4], quaternion=q)
except IKError:
# So let's swap to an alternative IK method...
# This returns 'max_configs' number of joint positions
input('Press key to run solve_ik_via_sampling...')
new_joint_pos = agent.solve_ik_via_sampling([x, y, z - 0.4],
quaternion=q)[0]
# Because the arm is in Forxe/Torque mode, we need to temporarily disable
