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Dave Lattanzi’s Arm Planning Algorithms

Dave Lattanzi’s Arm Planning Algorithms. Overview. Used Python, Pygame , and Numpy Extended LT’s skeleton code Configurations found using inverse jacobian “Phantom” linkage method Path planning using RRT Implicit map vs explicit map. Main Program Pseudocode.

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Dave Lattanzi’s Arm Planning Algorithms

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  1. Dave Lattanzi’s Arm Planning Algorithms

  2. Overview • Used Python, Pygame, and Numpy • Extended LT’s skeleton code • Configurations found using inverse jacobian • “Phantom” linkage method • Path planning using RRT • Implicit map vs explicit map

  3. Main Program Pseudocode Initialize(xyz_goal, obstacles, arm_params) Until effector_location == xyz_goal: new_config = iKinematics(xyz_goal) effector_location= fKinematics(new_config) startTree(startConfig) endTree(goalConfig) Until arm.intersection = True: RRTExtend(startTree,randomConfig) RRTExtend(endTree,randomConfig) BuildPath() AnimatePath()

  4. Inverse Kinematics iKinematics(arm, goalxyz): J = getJacobian(arm) InverseJ = pseudoinverse(J) Δxyz = β(goalxyz – arm.locationxyz) ΔΘ = InverseJ * Δxyz new_config = arm.config + ΔΘ return new_config

  5. Forward Kinematics fKinematics(arm, goal_config): T[i] = Transform(arm.joint[i], goal_config) for i = 1:DOF: arm.joints[i] = arm.T[i] * arm.joints[i] return

  6. RRTExtend RRTExtend(tree, randomConfig): nearestConfig = nearest(tree) Δ = (randomConfig – nearestConfig) / norm (randomConfig – nearestConfig) Until nearestConfig == randomConfig: nearestConfig = nearestConfig + Δ checkIntersections() checkCollisions()

  7. Planning Demo: 2 Obstacles

  8. Planning Demo: 4 Obstacles

  9. Issues and Future Upgrades • Restrict RRT to allowable halfspace • Refine dummy linkage method (damping?) • Optimize searches • Figure out tranpose/inverse issue

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