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Planning Motions with Intention

Planning Motions with Intention. Presented by: Yan Ke. Problem Specification. Task: Generate motions for human or robot arms to complete manipulation tasks. Goal: Find a collision-free path in configuration space. Tool: Inverse kinematics algorithm. Usage: Computer animation. Difficulties.

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Planning Motions with Intention

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  1. Planning Motions with Intention Presented by: Yan Ke NUS CS5247

  2. Problem Specification • Task: Generate motions for human or robot arms to complete manipulation tasks. • Goal: Find a collision-free path in configuration space. • Tool: Inverse kinematics algorithm. • Usage: Computer animation. NUS CS5247

  3. NUS CS5247

  4. Difficulties • Motion with Intention: Human and robot arms are moving with the intention of completing some task. • Restriction: Neurophysiology. • Grasping and Regrasping: Posture; multiple arms. • PSPACE-hard NUS CS5247

  5. Manipulation Planning Overview Section 1 NUS CS5247

  6. Inputs • Geometry of the arms • Movable object • Obstacles together with their locations • Initial and goal configuration NUS CS5247

  7. The Stable Space and Grasp Space • Stable space: The set of all configurations where the movable object M is statically stable. • Grasp Space: Arms grasping M and moving it stably. • Grasp Space Stable Space Free Space of the Configuration Space NUS CS5247

  8. Transit Paths and Transfer Paths • Transit Paths: Arms motions that do not move M • Transfer Paths: Arms motions that move M NUS CS5247

  9. Planning Result NUS CS5247

  10. Generating Transfer and Transit Paths Section 2 NUS CS5247

  11. Overview • The entire manipulation planning can be accomplished by following: • Generate a series of subtasks to achieve the goal configuration. • Plan a transit or transfer path for each subtasks. • Assumption: Transit tasks can be completed by transit paths; transfer tasks can be completed by transfer paths. NUS CS5247

  12. Generating Transfer Tasks • Grasp set: All various possible grasps for a certain M. • Grasp assignment: A pair associates with an element in grasp set and an identity of the grasping arm(s). • We first generate the path for M moving alone. • Secondly, we attach each configuration of M with a list of grasp assignment. NUS CS5247

  13. Generating Transfer Tasks • The attached list of grasp assignments are obtained by pruning out those no longer possible in the new configuration from the previous configuration. • If somehow we found the list of grasp assignment is empty, then a regrasping is necessary here. • We solve this problem by resetting the list, find all possible of grasp again, and associate them with arm(s). NUS CS5247

  14. Assumptions • An arm can attain a grasp with a finite set of different postures. • All arms not involved in the task is placed elsewhere without blocking the motions of working arms. • If M requires two arms to move, any one of them alone, can hold M stably to allow the other one to move in a transit path. NUS CS5247

  15. Result • A motion planning path for M, each configuration is attached with a list of grasp assignment. • The path is partitioned into several subpaths by regrasping. • Each subpath is a transfer task. • It does not guarantee to find the best path. NUS CS5247

  16. Generating Transit Paths • Transit paths are the paths moving the arms. • Connect the initial configuration to the first grasp assignment of the first transfer task. • Connect grasp assignments between different transfer tasks. • Connect the last grasp assignment to the goal configuartion. NUS CS5247

  17. Human-Arm Kinematics Section 3 NUS CS5247

  18. Neurophysiology • Goal: Determine the arm posture for a human arm given the position and orientation of its hand. • Two experimental result: • Arm and wrist posture are for the most part independent of each other. • Arm posture for pointing is mainly determined by an ST model. • ST model: Can determine shoulder and elbow joint angles given the position of hand. NUS CS5247

  19. Arm Posture • What do we have? • R, ψ, X • What do we want? • θ, β,α,η NUS CS5247

  20. Inverse Kinematics Algorithm NUS CS5247

  21. Illegal Posture Adjustment • Claim: εis the only one to violate its limits. • Solution: Decrease Φ. • Result: wrist position unchanged when Φ decrease. NUS CS5247

  22. Experimental Result NUS CS5247

  23. Experimental Result • Working environment: C and UNIX. • Time used: three and a half minutes. • Identify the transfer tasks: one and a half minutes. • Different grasp assignments in total: 2600. NUS CS5247

  24. Conclusion • A novel approach to solve the multi-arm manipulation planning problem. • Computation time is unbounded. If no path exist, the algorithm may run forever. • Aim to create a task-level animation package for human motions. NUS CS5247

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