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Planning Motions with Intention

This project aims to generate motion paths for human or robot arms to complete manipulation tasks in a collision-free manner, utilizing inverse kinematics algorithms for computer animation. The difficulties of motion with intention and grasping/regrasping are addressed by considering neuroscience principles. The stable space and grasp space concepts are key, alongside transit and transfer paths generation. The project focuses on generating transfer and transit tasks efficiently through grasp assignments and arm postures, with assumptions set to ensure task completion. The resulting motion planning path for each scenario is segmented into subpaths for practical implementation, prioritizing transit paths for arm movements. Human-arm kinematics, inverse kinematics algorithms, and experimental results contribute to a comprehensive solution, though potential challenges and unbounded computation time are acknowledged. The ultimate goal is to develop a task-level animation package for human motions.

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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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