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