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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.

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Path Planning for Multi Agent Systems

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  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

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