1 / 21

Path Planning for Multi Agent Systems

Path Planning for Multi Agent Systems. by Kemal Kaplan. Multi Agent Systems (MAS). A multi-agent system is a system in which there are several agents in the same environment which co-operate at least part of the time.

lis
Download Presentation

Path Planning for Multi Agent 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. Path Planning forMulti Agent Systems by Kemal Kaplan

  2. Multi Agent Systems (MAS) • Amulti-agent system is a system in which there are several agents inthe same environment which co-operate at least part of the time. • Complexity of the path planning systems for MAS (MASPP) increase exponentially with the number of moving agents.

  3. Problems with MASPP • Possible problems of applying ordinary PP methods to MAS are, • Collisions, • Deadlock situations, etc. • Problems with MASPP are, • Computational overhead, • Information exchange, • Communication overhead, etc.

  4. Classification of Obstacles • Usually other agents are modelled as unscheduled, non-negotiable, mobile obstacles in MASPPs. • Category of Obstacles from Arai et. al. (89)

  5. Proposed Techniques • Centralised Approaches • Decoupled Approaches • Combined Techniques

  6. Centralised Approaches • All robots in one composite system. + Find complete and optimum solution if exists. + Use complete information - Computational complexity is exponential w.r.t the number of robots in the system - Single point of failure

  7. Decoupled Approaches • First generate paths for robots (independently), then handle interactions. + Computation time is proportional to the number of neighbor robots. + Robust - Not complete - Deadlocks may occur

  8. Combined Techniques • Use cumulative information for global path planning, use local information for local planning “Think Global Act Local”

  9. Utilities For Combined Techniques • Global Planning Utilities: • The aim is planning the complete path from current position to goal position. • Any global path planner may be used. (e.g. A*, Wavefront, Probabilistic Roadmaps, etc.) • Requires graph representation achieved by cell decomposition or skeletonization techniques.

  10. Utilities For Combined Techniques (II) • Local Planning Utilities: • The aim is usally avoid obstacles. However, cooperation should be used also. • Any reactive path planner can be used. (e.g. PFP, VFH, etc.) • No global information or map representaion required. Decisions are fast and directly executable.

  11. Improvements for Combined Techniques • Priority assignment • Aging (e.g. the forces in a PFP varies in case of deadlocks) • Rule-Based methods (e.g. left agent first, or turn right first) • Resource allocation (leads to suboptimal solutions)

  12. Improvements for Combined Techniques (II) • Robot Groups • A leader and followers • Many leaders (or hierarchy of leaders and experience) • Virtual leader • Virtual dampers and virtual springs • Assigning dynamic information to edges and vertices

  13. Possibe MAS environmets for MASPP • Robocup 4-Legged League • Robocup Rescue • SIMUROSOT, MIROSOT (?) • Games (RTS, FPS) • ...

  14. MASPP Example [ARAI & OTA 89] • Measures • Computational Load • Total length of the generated trajectories • The radius of curvature of the generated trajectories • Total motion time • Preferred measure is the first one

  15. MASPP Example [ARAI & OTA 89] • Properties of agents

  16. MASPP Example [ARAI & OTA 89] • Problem 1

  17. MASPP Example [ARAI & OTA 89] • Problem 2

  18. MASPP Example [ARAI & OTA 89] • Virtual Impedance Method

  19. MASPP Example [ARAI & OTA 89]

  20. MASPP Example [ARAI & OTA 89]

  21. Questions? kaplanke@boun.edu.tr

More Related