# Path Planning for Multi Agent Systems by ikn20172

VIEWS: 133 PAGES: 21

• pg 1
```									 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.
   Complexity of the path planning
systems for MAS (MASPP) increase
exponentially with the number of
moving agents.
Problems with MASPP

   Possible problems of applying ordinary
PP methods to MAS are,
   Collisions,
   Problems with MASPP are,
   Information exchange,
Classification of Obstacles
   Usually other
OBSTACLES                            agents are
modelled as
unscheduled,
STATIC            MOBILE
non-negotiable,
mobile obstacles
NEGOTIABLE
NON-
NEGOTIABLE
in MASPPs.

   Category of
SCHEDULED       UNSCHEDULED
Obstacles from
Arai et. al. (89)
Proposed Techniques

   Centralised Approaches
   Decoupled Approaches
   Combined Techniques
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
Decoupled Approaches
   First generate paths for robots
(independently), then handle
interactions.
+ Computation time is proportional to
the number of neighbor robots.
+ Robust
- Not complete
Combined Techniques

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

“Think Global Act Local”
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.
etc.)
   Requires graph representation achieved by
cell decomposition or skeletonization
techniques.
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.
Improvements for Combined
Techniques
   Priority assignment
   Aging (e.g. the forces in a PFP varies in
   Rule-Based methods (e.g. left agent first,
or turn right first)
   Resource allocation (leads to suboptimal
solutions)
Improvements for Combined
Techniques (II)
   Robot Groups
experience)
   Virtual dampers and virtual springs
   Assigning dynamic information to edges
and vertices
Possibe MAS environmets for
MASPP
   Robocup 4-Legged League
   Robocup Rescue
   SIMUROSOT, MIROSOT (?)
   Games (RTS, FPS)
   ...
MASPP Example [ARAI & OTA 89]

   Measures
   Total length of the generated trajectories
   The radius of curvature of the generated trajectories
   Total motion time

   Preferred measure is the first one
MASPP Example [ARAI & OTA 89]

   Properties of agents
MASPP Example [ARAI & OTA 89]

   Problem 1
MASPP Example [ARAI & OTA 89]

   Problem 2
MASPP Example [ARAI & OTA 89]

   Virtual Impedance Method
MASPP Example [ARAI & OTA 89]
MASPP Example [ARAI & OTA 89]
Questions?
kaplanke@boun.edu.tr

```
To top