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Motion Algorithms: Planning, Simulating, Analyzing Motion of Physical Objects

Motion Algorithms: Planning, Simulating, Analyzing Motion of Physical Objects. Jean-Claude Latombe Computer Science Department Stanford University. Pernes-les-Fontaines. About Myself. Born a long time ago in South-East of France. About Myself. Born a long time ago in South-East of France

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Motion Algorithms: Planning, Simulating, Analyzing Motion of Physical Objects

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  1. Motion Algorithms:Planning, Simulating, Analyzing Motion of Physical Objects Jean-Claude Latombe Computer Science Department Stanford University

  2. Pernes-les-Fontaines About Myself • Born a long time ago in South-East of France

  3. About Myself • Born a long time ago in South-East of France • Studied in Grenoble(Eng. EE, MS EE, PhD CS 1977) • CS Professor, Grenoble (1980-84) • CEO, ITMI (1984-87) • Stanford (1987-…)

  4. Research Interests • 1980-84: Artificial Intelligence, Computer Vision, Robotics • 1987-92: Robot Motion Planning • 1993-98: Motion Planning • 1998-…: Motion Algorithms

  5. Fundamental Question Are two given points connected by a path?

  6. How Do You Get There? ?

  7. How Do You Get There? • Problems: • Geometric complexity • Space dimensionality

  8. Increasing Complexity

  9. Assembly planning Target finding New Problems

  10. From Simulation to Real Robots

  11. Space Robots robot obstacles air thrusters gas tank air bearing

  12. Modular Reconfigurable Robots Casal and Yim, 1999 Xerox, Parc

  13. Humanoid Robot [Kuffner and Inoue, 2000] (U. Tokyo) Stability constraints

  14. Radiosurgery

  15. From Robots to Other Agents: Digital Actors

  16. Simulation of Deformable Objects

  17. Study of Molecular Motion Ligand binding Protein folding

  18. Basic Tool: Configuration Space Approximate the free space by random sampling  Probabilistic Roadmaps [Lozano-Perez, 80]

  19. Probabilistic Roadmap (PRM) free space

  20. local path milestone mg mb Probabilistic Roadmap (PRM) free space

  21. [Quinlan, 94; Gottschalk, Lin, Manocha, 96] First Assumption of PRM Planning Collision tests can be done efficiently.  Several thousand collision checks per second for 2 objects of 500,000 triangles each on a 1-GHz PC

  22. Problem

  23. Exact Collision Checking of Path Segments • Idea: Use distance computation in workspace rather than pure collision checking D= 2Lx|dq1|+L|dq2|  3Lxmax{|dq1|,|dq2|} If D  d then no collision d q2 q1

  24. Exact Collision Checker in Action

  25. Second Assumption of PRM Planning A relatively small number of milestones and local paths are sufficient to capture the connectivity of the free space.

  26. Probabilistic Completeness In an expansive space, the probability that a PRM planner fails to find a path when one exists goes to 0 exponentially in the number of milestones (~ running time).

  27. Narrow-Passage Issue

  28. vi Pij vj Application to Biology Markov chain + first-step analysis  ensemble properties

  29. Current Projects • Robot motion planningFunding: General Motors, ABBCollaborator: Prinz (ME), Rock (AA) • Study of molecular motions (folding, binding)Funding: NSF-ITR (with Duke and UNC), BioXCollaborators: Guibas (CS), Brutlag (Biochemistry), Levitt (Structural Biology), Pande (Chemistry), Lee (Cellular B.) • Surgical simulation (deformable tissue, suturing, visual and haptic feedback)Funding: NSF, NIH, BioXCollaborators: Salisbury (CS+Surgery), Girod (Surgery), Krummel (Surgery) • Modeling and simulation of deformable objectsFunding: NSF-ITR (with UPenn and Rice)Collaborators: Guibas (CS), Fedkiw (CS)

  30. Tadjikistan Pakistan Afghanistan Third Pillar of Dana (California) Cho-Oyu, 8200m, ~27,000ft (Tibet) Muztagh Ata, 7,600m, 25,000ft (Xinjiang, China) Thailand

  31. Rock-Climbing Robot With Tim Bretl and Prof. Steve Rock

  32. Half-Dome, NW Face, Summer of 2010 … Tim Bretl

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