1 / 24

Using a PRM Planner to Compare Centralized and Decoupled Planning for Multi-Robot Systems

Using a PRM Planner to Compare Centralized and Decoupled Planning for Multi-Robot Systems By Gildardo Sánchez and Jean-Claude Latombe In Proc. IEEE Int. Conf. on Robotics and Automation 2002 Presented by Melvin Zhang Overview Motivation Coordinating multiple robots Centralized planning

Ava
Download Presentation

Using a PRM Planner to Compare Centralized and Decoupled Planning for Multi-Robot Systems

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. Using a PRM Planner to Compare Centralized and Decoupled Planning for Multi-Robot Systems By Gildardo Sánchez and Jean-Claude Latombe In Proc. IEEE Int. Conf. on Robotics and Automation 2002 Presented by Melvin Zhang NUS CS5247

  2. Overview • Motivation • Coordinating multiple robots • Centralized planning • Decoupled planning • SBL planner • Experiment setup • Results • Summary • Comments NUS CS5247

  3. Motivation • Some industrial settings (spot welding) requires 4-10 robots with 20-60 dof each • Manual programming • time consuming and error prone • Multi robot planning can be classified as • centralized • decoupled • Decoupled approach is prevalent, as lost of completeness is assumed to be small • How valid is this statement? NUS CS5247

  4. Coordinating multiple robots (Demo) NUS CS5247

  5. Coordinating multiple robots • Assuming p robots with n dof each • Centralized planning • Treat multiple robots as a single robot • Plan in the composite C-space • Complexity ~ enp • Decoupled planning • Plan for each robot independently • Coordinate them later • Complexity ~ pen NUS CS5247

  6. Centralized planning • Reduce problem to planning for single robot • Collisions between robots are self-collisions of the single composite robot • Advantages • Complete, if the underlying planner is complete • Drawbacks • Computationally expensive, • Not applicable when global state of all robots is unknown NUS CS5247

  7. Decoupled planning • Plans path of each robot independently • Coordinate them later • Several schemes • Velocity turning • Robot prioritization • Advantages • Faster as C-space has fewer dimensions • Drawbacks • Incomplete • No coordinated trajectory of paths found in first phase NUS CS5247

  8. Decoupled planning – Two schemes • Velocity tuning • Separately plan a path of each robot to avoid collision with obstacles • Compute the trajectory of the robots to avoid inter-robot collision • Global coordination – plan in [0,1]p • Pairwise coordination – plan in [0,1]2 • After path is fixed, dof of each robot is 1 • Pairwise coordination • plan s1 and s2 • plan s1,2 with s3, • ... plan s1,...,n-1 with sn NUS CS5247

  9. Decoupled planning – Two schemes • Robot prioritization • Plan path of the first robot in its C-space • Plan trajectory of ith robot assuming that robots 1,…,i-1 are moving obstacles NUS CS5247

  10. Decoupled planning - Incompleteness • Initial configuration Goal configuration • Paths generated in first phase • No coordinated solution found in second phase NUS CS5247

  11. SBL planner • Single-query • Roadmap is used to answer a single planning query • Bi-directional • Grow a tree of milestones from both start and end configuration • Lazy in checking collision • Avoid unnecessary collision checking on edges • 4-40 times faster than classical single-query bidirectional PRM planner NUS CS5247

  12. Characteristics of SBL planner • Plot of number of failure vs max milestones allowed (S) • Two thresholds Smin and Smax for a problem instance • If (S < Smin) planner fails consistently • If (S > Smax) planner succeeds consistently NUS CS5247

  13. Experiment setup • Planners • Centralized planning (C-SBL) • Decoupled planning, global coordination (DG-SBL) • Decoupled planning, pairwise coordination (DP-SBL) • Three problem instances, {PI, PII, PIII} • Number of robots involved, {2, 4, 6} • Number of runs • 100 for C-SBL • 20 for DG-SBL and DP-SBL • For each call to the SBL planner, at most 50,000 milestones are allowed NUS CS5247

  14. Problem I NUS CS5247

  15. Problem II NUS CS5247

  16. Problem III NUS CS5247

  17. Results – C-SBL • Result for C-SBL NUS CS5247

  18. Results – Failure rate • Rate of failure increases sharply for 4 and 6 robots • Failure occurs during coordination • Successful run of decoupled planner, no of milestones smaller than 50,000 -> failure due to incompleteness of decoupled approach NUS CS5247

  19. Results – Running time • Running time for all 3 planners are comparable • Centralize planning is feasible using SBL planner NUS CS5247

  20. Summary • Decoupled planning may not find a solution when tight coordination is required • Loss of completeness is significant in practice • Using SBL, planning time for decoupled and centralized planning is comparable • Centralized planning is technically feasible NUS CS5247

  21. Comments • Tight coordination is specified using specific problem instances • Similar to the concept of expansiveness, is it possible to develop some characterization of “tight coordination”? • Centralized and decoupled can be viewed as two extremes of coordination • Can we find a continuum of planners in which the level of coordination can be parameterized? • One idea is to use a hierarchy of robots NUS CS5247

  22. Thank you for listening • Questions ? NUS CS5247

  23. Blank slide NUS CS5247

  24. Blank slide NUS CS5247

More Related