Design of a generic path path planning system
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DESIGN OF A GENERIC PATH PATH PLANNING SYSTEM. AILAB Path Planning Workgroup. OUTLINE. Path Planning Basics Current Implementations System Design Conclusion. PATH PLANNING BASICS. Path Configuration Work Space Configuration Space (Cspace) Cell Decomposition

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Design of a generic path path planning system l.jpg



Path Planning Workgroup

Outline l.jpg

  • Path Planning Basics

  • Current Implementations

  • System Design

  • Conclusion

AILAB Path Planning Workgroup

Path planning basics l.jpg

  • Path

  • Configuration

  • Work Space

  • Configuration Space (Cspace)

    • Cell Decomposition

    • Roadmap (Skeletonization)

  • Free, Obstacle, Unknown Space

  • Dimension and Degrees of Freedom

AILAB Path Planning Workgroup

Cell decomposition l.jpg
Cell Decomposition

  • Regular Grids

  • Multiresolution Cells

  • Trapezoidal Cells

AILAB Path Planning Workgroup

Roadmap skeletonization l.jpg
Roadmap (Skeletonization)

  • Meadow Maps

  • Generalized Voronoi Diagrams

  • Visibility Graphs

  • Probabilistic Roadmaps

AILAB Path Planning Workgroup

Properties of path planners l.jpg
Properties of Path Planners

  • Dynamic vs. static

  • Global vs. local

  • Optimal vs. suboptimal

  • Complete vs. heuristic

  • Metric vs. topological

AILAB Path Planning Workgroup

Classification of obstacles l.jpg
Classification of Obstacles

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

AILAB Path Planning Workgroup

Path planning techniques l.jpg
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.

AILAB Path Planning Workgroup

Problems with ma pp l.jpg

Possible problems of applying ordinary PP methods to MAS are,


Deadlock situations, etc.

Problems with MA-PP are,

Computational overhead,

Information exchange,

Communication overhead, etc.

Problems with MA-PP

AILAB Path Planning Workgroup

Approaches l.jpg
Approaches are,

  • 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

AILAB Path Planning Workgroup

Improvements for ma pp l.jpg
Improvements for MA-PP are,

  • Priority assignment

  • Aging

  • Rule-Based methods

  • Resource allocation

  • Robot Groups

  • Virtual dampers and virtual springs

  • Assigning dynamic information to edges and vertices


AILAB Path Planning Workgroup

Characteristics of mas l.jpg
Characteristics of MAS are,

According to Dudek et. al. [Dudek96,53],

  • Team Size1, 2, limited, infinite

  • Communication RangeNone, Near, Infinite

  • Communication TopologyBroadcast, Addressed, Tree, Graph

  • Communication BandwidthHigh, Motion related, Low, Zero

  • Team CompositionHomogeneous, Heterogeneous

AILAB Path Planning Workgroup

Characteristics of domain l.jpg
Characteristics of Domain are,

  • Initial InformationNone, Partial, Complete

  • Number of Targets1, Many

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

  • Stationary TargetsTrue, False

AILAB Path Planning Workgroup

Complexity of path planning l.jpg
Complexity of Path Planning are,

  • 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

AILAB Path Planning Workgroup

Imperfect solutions l.jpg
Imperfect solutions are,

  • Used in case of compex problems,

    • Approximation

    • Probabilistic

    • Heuristic

    • Special cases

AILAB Path Planning Workgroup

Current implementations l.jpg

  • Sampling Based Algorithms

    • Incomplete, but efficient and practical

  • Types

    • Multiple Query

    • Single Query

AILAB Path Planning Workgroup

Multiple query l.jpg
Multiple Query are,

  • A map is generated for multiple queries

  • Fill the space adequately

  • Probabilistic Roadmap

    • Uniform sampling of C-free

    • Local planner attempts connections

    • Biased sampling

AILAB Path Planning Workgroup

Single query l.jpg
Single Query are,

  • 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

AILAB Path Planning Workgroup

Demos l.jpg
Demos are,

  • Path Planning

    • Probabilistic Roadmap (PRM)

      • Different sampling methods

    • Rapidly-exploring Random Trees (RRTs)

      • RRT

      • RRT-Connect

AILAB Path Planning Workgroup

System design l.jpg

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

AILAB Path Planning Workgroup

Overview l.jpg
Overview are,


  • Model

  • Geom

  • Problem

  • Solver

  • Scene

  • Render

  • Gui

AILAB Path Planning Workgroup

Model l.jpg
Model are,

  • 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.

AILAB Path Planning Workgroup

Slide23 l.jpg
Geom are,

  • 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 ProximityQueryPackage)

AILAB Path Planning Workgroup

Problem l.jpg
Problem are,

  • 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).

AILAB Path Planning Workgroup

Planner l.jpg
Planner are,

  • The most important module.

  • Base for all path planners...

AILAB Path Planning Workgroup

Conclusion l.jpg

  • 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.

  • ...

AILAB Path Planning Workgroup

Questions l.jpg

Thank you...

AILAB Path Planning Workgroup