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CS 326A: Motion Planning

CS 326A: Motion Planning. Jean-Claude Latombe CA: Aditya Mandayam. Motion planning is the ability for an agent to compute its own motions in order to achieve certain goals. All autonomous robots and digital actors should eventually have this ability. Piano Mover’s Problem. Sense. Plan.

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CS 326A: Motion Planning

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  1. CS 326A: Motion Planning Jean-Claude Latombe CA: Aditya Mandayam

  2. Motion planning is the ability for an agent to compute its own motions in order to achieve certain goals. All autonomous robots and digital actors should eventually have this ability

  3. Piano Mover’s Problem

  4. Sense Plan Move

  5. Goal ARL Robot

  6. Sense Plan Move Motion library Learn

  7. Goal of Motion Planning • Compute motion strategies, e.g.: • geometric paths • time-parameterized trajectories • sequence of sensor-based motion commands • To achieve high-level goals,e.g.: • go to A without colliding with obstacles • assemble product P • build map of environment E • find object O

  8. Fundamental Question Are two given points connected by a path? Valid region Forbidden region

  9. E.g.: ▪Collision with obstacle ▪Lack of visibility of an object ▪Lack of stability Fundamental Question Are two given points connected by a path? Valid region Forbidden region

  10. Basic Problem • Statement:Compute a collision-free path for a rigid or articulated object among static obstacles • Inputs: • Geometry of moving object and obstacles • Kinematics of moving object (degrees of freedom) • Initial and goal configurations (placements) • Output: Continuous sequence of collision-free robot configurations connecting the initial and goal configurations

  11. Is It Easy?

  12. Tool: Configuration Space • Problems: • Geometric complexity • Space dimensionality

  13. Sampling-based Criticality-based Continuous space C-space Discretization Search

  14. Moving obstacles Multiple robots Movable objects Assembly planning Goal is to acquire information by sensing Model building Object finding/tracking Inspection Nonholonomic constraints Dynamic constraints Stability constraints Optimal planning Uncertainty in model, control and sensing Exploiting task mechanics (sensorless motions, under-actualted systems) Physical models and deformable objects Integration of planning and control Integration with higher-level planning Extensions of Basic Problem

  15. Some Applications

  16. Humanoid Robots HRP-2, AIST, Japan

  17. Lunar Vehicle (ATHLETE, NASA/JPL)

  18. Climbing Robot http://www.youtube.com/watch?v=biSx-aKN690

  19. Dexterous Manipulation

  20. Modular Reconfigurable Robots

  21. Manipulation of Deformable Objects Topologically defined goal

  22. Digital Characters Toy Story (Pixar/Disney) Antz (Dreamworks) A Bug’s Life (Pixar/Disney) Tomb Raider 3 (Eidos Interactive) The Legend of Zelda (Nintendo) Final Fantasy VIII (SquareOne)

  23. Digital Characters

  24. Animation of Crowds

  25. Design for Manufacturing and Servicing

  26. Design for Manufacturing and Servicing

  27. Design for Manufacturing and Servicing

  28. Assembly Sequence Planning

  29. Cable Harness/ Pipe design

  30. Map Building Where to move next?

  31. Navigation Through Virtual Environments

  32. Virtual Angiography / Bronchoscopy / Colonoscopy

  33. Radiosurgical Planning CyberKnife (Accuray)

  34. 24-inch turning radius 9-inch turning radius Building Code Verification

  35. Primary escape route Potential congesting areas Secondary escape route Egress Simulation

  36. Self-Parking

  37. Transportation of A380 Fuselage through Small Villages Kineo

  38. Study of Motion of Bio-Molecules Inhibitor binding to HIV protease

  39. Goals of CS326A • Present a coherent framework for motion planning problems • Emphasis of “practical” algorithms with some guarantees of performance over “theoretical” or purely “heuristic” algorithms

  40. General Framework Continuous representation (configuration space and related spaces + constraints) Discretization (probabilistic sampling, criticality-based decomposition) Graph searching (blind, best-first, A*)

  41. Practical Algorithms (1/2) • A complete motion planner always returns a solution plan when one exists and indicates that no such plan exists otherwise. • Most motion planning problems are hard, meaning that complete planners take exponential time in # of degrees of freedom, objects, etc.

  42. Practical Algorithms (2/2) • Theoretical algorithms strive for completeness and minimal worst-case complexity. Difficult to implement and not robust. • Heuristic algorithms strive for efficiency in commonly encountered situations. Usually no performance guarantee. •  Weaker completeness Simplifying assumptions Exponential algorithms that work in practice

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