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A Motion Planner for the Human Hand

A Motion Planner for the Human Hand. Project by: Qi-Xing & Samir Menon. Motion Planning for the Human Hand. θ i. θ 2. θ 20. θ 1. Find Parametrization Vector, Θ { θ 1, θ 2..}. Generate Hand Skeleton. Define Configuration Space.

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A Motion Planner for the Human Hand

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  1. A Motion Planner for the Human Hand Project by: Qi-Xing & Samir Menon

  2. Motion Planning for the Human Hand θi θ2 θ20 θ1 Find Parametrization Vector, Θ{θ1, θ2..} Generate Hand Skeleton Define Configuration Space User defines two poses – Find Path & Smoothen to get Realistic Motion Sample Configuration Space for Milestones & Collisions Connect Adjacent Configurations

  3. The Human Hand • Motion is induced by the application of musculo-skeletal control • We demonstrate planned motion of a human hand • Simulated hand has a 20 degree of freedom skeleton • Control is applied to the joint angles of the skeleton • Planning takes place in 20-dimensional joint configuration space • The planned path is executed in a simulated model of the human hand

  4. The Hand Skeleton • The human hand may be modeled using a 20 DoF skeleton parameterization • Configuration of the human hand is represented by a 20 dimensional joint angle vector Hand Space

  5. Modeling the Hand • ‘Hand-space’ models an actual human hand • The hand is represented by a mesh representation of a laser scanned hand • The parameterization allows the emulation of a real hand Hand Space Configuration

  6. Configuration Space • Hand motion is in 20 dimensional configuration space along the planned path θi θ20 Disallowed Hand Config = C–Space Obstacle Θ1 Path of Motion Θ2 θ2 Θ3 Θ4 Θ5 Θi={θ1, θ2,…, θ20} Represents a hand configuration θ1 θ0 Milestones = Sampled Hand Configuration

  7. Uniform Sampling • A uniformly random sampler θi θ20 θ2 θ1 θ0

  8. Adaptive-Gaussian-Random Sampling • An adaptive gaussian sampler θi θ20 Gaussian Sample θ2 Adaptive Sample Random Sample θ1 θ0

  9. Connecting Samples • Obtain a roadmap in the form of a search graph • Connect each sample to 10 closest samples and check for collision • Reject connections with collisions θi θ20 θ2 θ1 θ0

  10. Collision Detection Strategy θi θ20 θ2 Collision!!! Path Added To Roadmap! θ1 θ0

  11. Planning Hand Motion • Add start and goal configuration nodes to graph • Search for a path in the graph Resulting Path is Jerky due to imperfect sampling!! θi θ20 θ2 Goal θ1 Start θ0

  12. Planning Hand Motion (contd.) • Video of jerky motion

  13. Smoothing Motion θi θ20 Smooth Path is obtained!! θ2 Goal θ1 Start θ0

  14. Smoothing Motion (contd.) • Video of smooth motion

  15. Demo Eg.4 • System demo Eg.1 Eg.2 Eg.3

  16. Results: Sampling

  17. Results: Smoothing

  18. Discussion • Smoothing the path greatly improves motion quality • Adaptive Gaussian Sampling can drastically reduce the required samples but it also requires more precomputation • Straight line motion in higher dimensional space produces better quality than curved or spline motion.

  19. Future Work • Areas for improvement: • The project may be extended to involve: • Control applied to muscular configuration space • Improved skeleton that closely matches a real hand • System dynamics such as inertia and damping

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