Path Planning for Multi Agent Systems by Kemal Kaplan.

Path Planning forPath Planning forMulti Agent SystemsMulti Agent Systems

byby

Kemal KaplanKemal Kaplan

Multi Agent Systems (MAS)Multi Agent Systems (MAS)

AA multi-agent system multi-agent system is a system is a system in which there are several agents in which there are several agents inin the same environment which co-the same environment which co-operate at least part of the time.operate at least part of the time.

Complexity of the path planning Complexity of the path planning systems for MAS (MASPP) increase systems for MAS (MASPP) increase exponentially with the number of exponentially with the number of moving agents.moving agents.

Problems with MASPPProblems with MASPP

Possible problems of applying Possible problems of applying ordinary PP methods to MAS are,ordinary PP methods to MAS are, Collisions,Collisions, Deadlock situations, etc.Deadlock situations, etc.

Problems with MASPP are,Problems with MASPP are, Computational overhead,Computational overhead, Information exchange,Information exchange, Communication overhead, etc.Communication overhead, etc.

Classification of ObstaclesClassification of Obstacles

Usually other Usually other agents are agents are modelled as modelled as unscheduled, unscheduled, non-negotiable, non-negotiable, mobile mobile obstacles in obstacles in MASPPs. MASPPs.

Category of Category of Obstacles from Obstacles from Arai et. al. (89)Arai et. al. (89)

OBSTACLES

STATIC MOBILE

NEGOTIABLENON-

NEGOTIABLE

SCHEDULED UNSCHEDULED

Proposed TechniquesProposed Techniques

Centralised ApproachesCentralised Approaches Decoupled ApproachesDecoupled Approaches Combined TechniquesCombined Techniques

Centralised ApproachesCentralised Approaches

All robots in one composite system.All robots in one composite system.++ Find complete and optimum Find complete and optimum solution solution

if exists.if exists.++ Use complete informationUse complete information-- Computational complexity is Computational complexity is exponential w.r.t the number of robots exponential w.r.t the number of robots

in the systemin the system-- Single point of failureSingle point of failure

Decoupled ApproachesDecoupled Approaches

First generate paths for robots First generate paths for robots (independently), then handle (independently), then handle interactions.interactions.

++ Computation time is proportional Computation time is proportional to to the number of neighbor robots.the number of neighbor robots.

++ RobustRobust

- - Not completeNot complete

-- Deadlocks may occurDeadlocks may occur

Combined TechniquesCombined Techniques

Use cumulative information for global Use cumulative information for global path planning, use local information path planning, use local information for local planningfor local planning

““Think Global Act Local”Think Global Act Local”

Utilities For Combined Utilities For Combined TechniquesTechniques

Global Planning Utilities:Global Planning Utilities: The aim is planning the complete path The aim is planning the complete path

from current position to goal position.from current position to goal position. Any global path planner may be used. Any global path planner may be used.

(e.g. A*, Wavefront, Probabilistic (e.g. A*, Wavefront, Probabilistic Roadmaps, etc.)Roadmaps, etc.)

Requires graph representation achieved Requires graph representation achieved by cell decomposition or skeletonization by cell decomposition or skeletonization techniques.techniques.

Utilities For Combined Utilities For Combined Techniques (II)Techniques (II)

Local Planning Utilities:Local Planning Utilities: The aim is usally avoid obstacles. The aim is usally avoid obstacles.

However, cooperation should be used However, cooperation should be used also.also.

Any reactive path planner can be used. Any reactive path planner can be used. (e.g. PFP, VFH, etc.)(e.g. PFP, VFH, etc.)

No global information or map No global information or map representaion required. Decisions are representaion required. Decisions are fast and directly executable.fast and directly executable.

Improvements for Combined Improvements for Combined TechniquesTechniques

Priority assignment Priority assignment Aging (e.g. the forces in a PFP varies Aging (e.g. the forces in a PFP varies

in case of deadlocks)in case of deadlocks) Rule-Based methods (e.g. left agent Rule-Based methods (e.g. left agent

first, or turn right first)first, or turn right first) Resource allocation (leads to Resource allocation (leads to

suboptimal solutions)suboptimal solutions)

Improvements for Combined Improvements for Combined Techniques (II)Techniques (II)

Robot GroupsRobot Groups A leader and followersA leader and followers Many leaders (or hierarchy of leaders Many leaders (or hierarchy of leaders

and experience)and experience) Virtual leader Virtual leader

Virtual dampers and virtual springsVirtual dampers and virtual springs Assigning dynamic information to Assigning dynamic information to

edges and verticesedges and vertices

Possibe MAS environmets for Possibe MAS environmets for MASPPMASPP

Robocup 4-Legged LeagueRobocup 4-Legged League Robocup RescueRobocup Rescue SIMUROSOT, MIROSOT (?)SIMUROSOT, MIROSOT (?) Games (RTS, FPS)Games (RTS, FPS) ......

MASPP Example [ARAI & OTA MASPP Example [ARAI & OTA 89]89]

MeasuresMeasures Computational LoadComputational Load Total length of the generated trajectoriesTotal length of the generated trajectories The radius of curvature of the generated The radius of curvature of the generated

trajectoriestrajectories Total motion timeTotal motion time

Preferred measure is the first onePreferred measure is the first one


Properties of Properties of agentsagents


Problem 1Problem 1


Problem 2Problem 2


Virtual Impedance MethodVirtual Impedance Method

Questions?Questions?

[email protected]@boun.edu.tr

Path Planning for Multi Agent Systems by Kemal Kaplan.

Documents

Transcript of Path Planning for Multi Agent Systems by Kemal Kaplan.