1 / 28

PRM and Multi-Space Planning Problems : How to handle many motion planning queries?

This article discusses the challenges of handling multiple motion planning queries in various spaces and proposes approaches such as lazy PRM planning and query feasibility estimation.

dmcdade
Download Presentation

PRM and Multi-Space Planning Problems : How to handle many motion planning queries?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. PRM and Multi-Space Planning Problems:How to handle many motion planning queries? Jean-Claude LatombeComputer Science DepartmentStanford University (based on discussions with Tim Bretl and Kris Hauser)

  2. PRM Planning in Single Space • Applicable to robots with many dofs • In expansive configuration spaces:Probabilistically complete + fast convergence • But unable to detect that no solution exists  Cutoff on running time

  3. Convergence of a PRM Planner ??? What should be the cutoff time?

  4. Planning in Multiple SpacesExample 1: Climbing Robot 4-contact move 3-contact move

  5. difficult queriesor bad luck? Climbing Robot Dilemma[Bretl, 2005] • Thousands of spaces  many PRM queries • Most queries have no solution • Running times for feasible queries are highly variable • Large time cutoff  Prohibitive time is wasted on infeasible queries • Small time cutoff  Critical queries might not be solved

  6. Other Examples • Navigation on irregular terrain [Hauser, 2008]

  7. Other Examples • Dexterous manipulation

  8. Other Examples • Mechanical assembly

  9. Other Examples • Spatial re-arrangements of movable objects [Stillman and Kuffner, 2007]

  10. Other Examples • Modular reconfigurable robots [Yim]

  11. Other Examples Change battery Go to toolbox • Integration of task and motion planning Grasp screwdriver Go to old battery Unscrew screws Ungrasp screwdriver Grasp old battery Remove old battery

  12. Basic Architecture High-level Planner (graph searching) Many queries are infeasible  “climbing-robot” dilemma query result Motion Planner (PRM) Each query involves a distinct configuration space, with its own dimensionality, parameterization, and/or constraints.  queries cannot be processed usingone single precomputedroadmap

  13. Possible Approaches • Estimating query feasibility • Lazy PRM planning High-level Planner (graph searching) query result Motion Planner (PRM)

  14. Learning Transition Feasibility[Hauser, 2008] • Create a large dataset of labeled transitions • Train a classifierQ: transition {feasible, non-feasible} • Use classifier to select sequences of spaces with likely feasibletransitions between them • But no work yet on learning feasibility of entire queries (that require connecting two transitions) 4 contacts 3 contacts Non-feasible if empty

  15. Possible Approaches • Estimating query feasibility • Lazy PRM planning High-level Planner (graph searching) query result Motion Planner (PRM)

  16. Lazy PRM Planning[Bohlin & Kavraki, 2000; Sanchez-Ante, 2001] • Observation:PRM planning wastes much time testing that sampled configurations and connections are valid (e.g., free of collision). • Idea:Perform a computation only when there is enough evidence that it may be useful.

  17. g s Lazy Collision Checking of Connections [Sanchez-Ante, 2001] X

  18. g s Lazy Collision Checking of Connections [Sanchez-Ante, 2001]

  19. Rationale • Configuration spaces are rarely chaotic: so, the connection between close valid configurations has high probability of being valid • Most of the time spent by a PRM planner is in testing connections • Most valid connections will not be part of the final solution • Testing connections is more expensive for valid connections than for invalid ones Postpone testing a connection until the test is likely to be useful

  20. Extending Lazy PRM Planning Create a bag of fine-grain computational probes: Nodesampling Node Connection

  21. Extending Lazy PRM Planning • Sample a node and partially test if it is valid p1 p8 p7 p6 p5 p4 p3 p2 r’ r d d > r+r’  p1 = 1 d ≤ r+r’  p1 ~ d/r+r’

  22. Extending Lazy PRM Planning • Create connection and partially test if it is valid p23 p38 p12 p24 p4 p1 p8 p7 p5 p3 p2 p6 p47 p45 p46

  23. Extending Lazy PRM Planning • Test further that a node is valid p23 p38 p12 p24 p8 p1 p3 p2 p6 p5 p4’ p7 p47 p45 p46

  24. Extending Lazy PRM Planning • Test further that a connection is valid p23 p38 p12 p24 p4’ p1 p8 p7 p5 p3 p2 p6 p47’ p45 p46

  25. Potential Advantages • More choices  opportunity for much smarter, more efficient strategies • More flexibility in distributing computation over several spaces, e.g., focus on queries that have the highest probability of being feasible • Compatibility with probabilistic modeling of uncertainty, e.g., probabilistic distribution of obstacles

  26. Conclusion • We will have to live with imperfect motion planners like PRM planners • Important problems require handling many motion planning queries in distinct spaces  “climbing-robot” dilemma • Possible approaches to address this dilemma: • Fast and reliable evaluation of query feasibility (e.g., using trained classifiers) • Extended lazy PRM planning

  27. Narrow Passages • I don’t think they are the main issue in PRM planning. • They are unlikely to occur by chance. • Intentionally creating complex narrow passages is not easy. Alpha puzzle

More Related