A PATH PLANNING ARCHITECTURE FOR MULTI AGENT SYSTEMS by ewghwehws

VIEWS: 10 PAGES: 27

									DESIGN OF A GENERIC
PATH PATH PLANNING
SYSTEM




AILAB
Path Planning Workgroup
OUTLINE

•   Path Planning Basics
•   Current Implementations
•   System Design
•   Conclusion




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PATH PLANNING BASICS

•   Path
•   Configuration
•   Work Space
•   Configuration Space (Cspace)
     – Cell Decomposition
     – Roadmap (Skeletonization)
• Free, Obstacle, Unknown Space
• Dimension and Degrees of Freedom
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Cell Decomposition

• Regular Grids
• Multiresolution Cells
• Trapezoidal Cells




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Roadmap (Skeletonization)


•   Meadow Maps
•   Generalized Voronoi Diagrams
•   Visibility Graphs
•   Probabilistic Roadmaps




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Properties of Path Planners


•   Dynamic vs. static
•   Global vs. local
•   Optimal vs. suboptimal
•   Complete vs. heuristic
•   Metric vs. topological




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Classification of Obstacles
                         OBSTACLES




                STATIC            MOBILE




                                         NON-
                     NEGOTIABLE
                                      NEGOTIABLE




                            SCHEDULED       UNSCHEDULED




Category of Obstacles from Arai et. al. [Arai89, 28]

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Path Planning Techniques

• Reactive Methods
     – Artificial Potential Fields
     – Vector Field Histogram Method
• Graph Traversing Methods
     – A* Algorithm
     – Best First / Breadth First / Greedy Search
• Wavefront Method
• Other Methods
     – Wall following, Space filling curves,
       Splines,Topological maps, etc.

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Problems with MA-PP

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

• Cenralised: All robots in one composite system.
     +   Find complete and optimum solution if exists.
     +   Use complete information
     -   Exponential computational complexity w.r.t # of robots
     -   Single point of failure

• Decoupled: First generate paths for robots
  (independently), then handle interactions.
     +   Proportional computation time w.r.t # of robots
     +   Robust
     -   Not complete
     -   Deadlocks may occur
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Improvements for MA-PP

•     Priority assignment
•     Aging
•     Rule-Based methods
•     Resource allocation
•     Robot Groups
•     Virtual dampers and virtual springs
•     Assigning dynamic information to edges and
      vertices
...

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Characteristics of MAS
According to Dudek et. al. [Dudek96,53],

• Team Size
         1, 2, limited, infinite

• Communication Range
         None, Near, Infinite

• Communication Topology
         Broadcast, Addressed, Tree, Graph

• Communication Bandwidth
         High, Motion related, Low, Zero

• Team Composition
         Homogeneous, Heterogeneous
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Characteristics of Domain

• Initial Information
          None, Partial, Complete

• Number of Targets
          1, Many

• Target Available
          True (i.e. go to target), False (i.e. explore for target)

• Stationary Targets
          True, False
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Complexity of Path Planning

• In 3D work space finding exact solution
  is NP-HARD. [Xavier92, 54]
• Path planning is PSPACE-HARD.
  [Reif79,55]
• The compexity increases exponentially
  with,
     – Number of DOF [Canny88, 9]
     – Number of agents

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Imperfect solutions

• Used in case of compex problems,
     – Approximation
     – Probabilistic
     – Heuristic
     – Special cases




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CURRENT IMPLEMENTATIONS


• Sampling Based Algorithms
     – Incomplete, but efficient and practical


• Types
     – Multiple Query
     – Single Query




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Multiple Query

• A map is generated for multiple queries
• Fill the space adequately

• Probabilistic Roadmap
     – Uniform sampling of C-free
     – Local planner attempts connections
     – Biased sampling


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Single Query

• Suited for high dimensions
• Find a path as quick as possible

• RRTs
     – Grow from an initial state
          • RRT-Connect : Grow from both initial and goal
     – Expand by performing incremental motions


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Demos

• Path Planning
     – Probabilistic Roadmap (PRM)
          • Different sampling methods
     – Rapidly-exploring Random Trees (RRTs)
          • RRT
          • RRT-Connect




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SYSTEM DESIGN



* Following slides are based on
  Lavelle’s Motion Strategy Library,
  implemented in C++




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Overview
                                MODULES:
                                • Model
                                • Geom
                                • Problem
                                • Solver
                                • Scene
                                • Render
                                • Gui
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Model




• Contain incremental simulators that model the kinematics and
  dynamics of a variety of mechanical systems. The methods allow
  planning algorithms to compute the future system state, given the
  current state, an interval of time, and a control input applied over
  that interval.
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Geom

                                • These define the
                                  geometric
                                  representations of all
                                  obstacles in the world,
                                  and of each part of the
                                  robot. The methods allow
                                  planning algorithms to
                                  determine whether any of
                                  the robot parts are in
                                  collision with each other
                                  or with obstacles in the
                                  world.
                                   (PQP - the Proximity
                                  Query Package )

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Problem

• This is an interface class to a planner,
  which abstracts the designer of a planning
  algorithm away from particular details such
  as collision detection, and dynamical
  simulations. Each instance of a problem
  includes both an instance of Model and of
  Geometry. An initial state and final state
  are also included, which leads to a
  problem to be solved by a solver (typically
  a planning algorithm).
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Planner


                                • The most
                                  important
                                  module.

                                • Base for all path
                                  planners...



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CONCLUSION

• Path planning is a challenging task with
  many different applications.
• Each application may device its own path
  planning strategy.
• A generic path planning library may
  provide solution or guidelines for other
  path planners.
• ...
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QUESTIONS?



                                     Thank you...
                                kaplanke@boun.edu.tr
                                fuatgeleri@gmail.com




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