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Motion Planning:

Explore the world of motion planning with a focus on computing strategies for tasks like geometric paths, assembly planning, and object finding/tracking. Dive into the challenges and advancements in this field, from handling moving obstacles to integrating planning and control. Discover applications and computational approaches in motion planning, from criticality-based methods to random-sampling techniques. Learn about the historical landmarks and future prospects in this fascinating journey of robots, digital actors, surgical instruments, molecules, and other artifacts.

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Motion Planning:

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  1. Motion Planning: A Journey of Robots, Digital Actors, Surgical Instruments, Molecules and Other Artifacts Jean-Claude Latombe Computer Science DepartmentStanford University

  2. 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 from A to B without colliding with obstacles • assemble product P • build map of environment E • find object O

  3. 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 from A to B without colliding with obstacles • assemble product P • build map of environment E • find object O

  4. 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 from A to B without colliding with obstacles • assemble product P • build map of environment E • find object O

  5. Basic Problem

  6. Moving obstacles Multiple robots Movable objects Assembly planning Goal is to acquire information by sensing Model building Object finding/tracking Nonholonomic constraints Dynamic constraints Optimal planning Uncertainty in control and sensing Exploiting task mechanics (sensorless motions) Physical models and deformable objects Integration of planning and control Extensions to the Basic Problem

  7. Moving obstacles Multiple robots Movable objects Assembly planning Goal is to acquire information by sensing Model building Object finding/tracking Nonholonomic constraints Dynamic constraints Optimal planning Uncertainty in control and sensing Exploiting task mechanics (sensorless motions) Physical models and deformable objects Integration of planning and control Extensions to the Basic Problem

  8. Moving obstacles Multiple robots Movable objects Assembly planning Goal is to acquire information by sensing Model building Object finding/tracking Nonholonomic constraints Dynamic constraints Optimal planning Uncertainty in control and sensing Exploiting task mechanics (sensorless motions) Physical models and deformable objects Integration of planning and control Extensions to the Basic Problem

  9. Moving obstacles Multiple robots Movable objects Assembly planning Goal is to acquire information by sensing Model building Object finding/tracking Nonholonomic constraints Dynamic constraints Optimal planning Uncertainty in control and sensing Exploiting task mechanics (sensorless motions) Physical models and deformable objects Integration of planning and control Extensions to the Basic Problem

  10. Outline • Some historical steps and achievements • Applications • Computational approaches: • Criticality-based motion planning • Random-sampling motion planning • Some challenging problems ahead

  11. Early Work Shakey (Nilsson, 1969): Visibility graph

  12. C = S1 x S1 Mathematical Foundations Lozano-Perez, 1980: Configuration Space

  13. Computational Analysis Reif, 1979: Hardness (lower-bound results)

  14. Exact General-Purpose Path Planners - Schwarz and Sharir, 1983: Exact cell decomposition based on Collins technique - Canny, 1987: Silhouette method

  15. Heuristic Planners Khatib, 1986: Potential Fields

  16. Nonholonomic Robots Laumond, 1986

  17. Underactuated Robots Lynch, Shiroma, Arai, and Tanie, 1998

  18. Part Orientation Godlberg, 1993

  19. Assembly Sequence Planning Wilson, 1994: Non-Directional Blocking Graphs

  20. Manipulation Planning Tsai-Yen Li, 1994

  21. Deformable Objects Kavraki, Lamiraux, and Holleman 1998

  22. Target Finding Guibas, Latombe, LaValle, Lin, and Motwani, 1997

  23. Integration of Planning and Control Brock and Khatib, 1999

  24. Outline • Some historical steps and achievements • Applications • Computational approaches: • Criticality-based motion planning • Random-sampling motion planning • Some challenging problems ahead

  25. Robot Programming and Placement David Hsu, 1999

  26. Design for Manufacturing and Servicing General Motors General Electric General Motors

  27. Design of Large Facilities EDF and LAAS-CNRS (MOLOG project), 1999

  28. Verification of Building Code Charles Han, 1998

  29. Graphic Animation of Digital Actors The MotionFactory Koga, Kondo, Kuffner, and Latombe, 1994

  30. Plan Sense Act Graphic Animation of Digital Actors Digital Actor = Virtual Robot! Kuffner, 1999

  31. Graphic Animation of Digital Actors Simulated Vision • Segment environment • Render false-color scene offscreen • Scan pixels & record IDs Actor camera image Vision module image

  32. Graphic Animation of Digital Actors

  33. Surgical Planning Cyberknife System (Accuray, Inc.) CARABEAMER Planner Tombropoulos, 1997

  34. Prediction of Molecular Motions Amit Singh, 1999

  35. Outline • Some historical steps and achievements • Applications • Computational approaches: • Criticality-based motion planning • Random-sampling motion planning • Some challenging problems ahead

  36. Approaches to Motion Planning • Goal: Answer queries about the connectivity of a certain space (e.g., the collision-free subset of configuration space)

  37. Approaches to Motion Planning • Old view (Latombe, 1991): • Roadmaps • Cell decomposition • Potential field

  38. Approaches to Motion Planning • Old view (Latombe, 1991): • Roadmaps • Cell decomposition • Potential field • New View (Latombe, 2000): • Finding criticalities • Random sampling

  39. Criticality-Based Motion Planning Retraction on Voronoi Diagram(O’Dunlaing and Yap, 1982)

  40. Criticality-Based Motion Planning Part orientation (Goldberg, 1993)

  41. Criticality-Based Motion Planning Non-Directional Blocking Graphs for assembly planning (Wilson, 1994)

  42. Criticality-Based Motion Planning Non-Directional Preimage for landmark-based navigation (Lazanas, 1995)

  43. Criticality-Based Motion Planning Non-Directional Preimage for landmark-based navigation (Lazanas, 1995)

  44. Criticality-Based Motion Planning Target finding (Guibas, Latombe, LaValle, Lin, and Motwani, 1997)

  45. Criticality-Based Motion Planning Target finding (Guibas, Latombe, LaValle, Lin, and Motwani, 1997)

  46. Criticality-Based Motion Planning Target finding (Guibas, Latombe, LaValle, Lin, and Motwani, 1997)

  47. 0 : the target does not hide beyond the edge 1 : the target may hide beyond the edge Criticality-Based Motion Planning Target finding (Guibas, Latombe, LaValle, Lin, and Motwani, 1997) Example of an information state = (1,1,0)

  48. Recontaminated area Criticality-Based Motion Planning Target finding (Guibas, Latombe, LaValle, Lin, and Motwani, 1997)

  49. Criticality-Based Motion Planning • Advantage: • Completeness • Drawbacks: • Computational complexity • Difficult to implement

  50. Outline • Some historical steps and achievements • Applications • Computational approaches: • Criticality-based motion planning • Random-sampling motion planning • Some challenging problems ahead

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