On experimental research in sampling based motion planning
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On Experimental Research in Sampling-based Motion Planning. Roland Geraerts Workshop on Benchmarks in Robotics Research IROS 2006. c. c’. Probabilistic Roadmap Method. Construction ( G = V , E ) Loop c  a free sample add c to the vertices V N c  a set of nodes

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On experimental research in sampling based motion planning

On Experimental Research in Sampling-based Motion Planning

Roland Geraerts

Workshop on Benchmarks in Robotics Research

IROS 2006


Probabilistic roadmap method

c

c’

Probabilistic Roadmap Method

Construction (G =V,E )

Loop

c a free sample

add c to the vertices V

Nc a set of nodes

for allc’ in Nc in increasing distance

ifc’ and c are not connected in Gthen

if local path between c and c’ exists then

add the edge c’c to E

Free space

Forbidden space

Sample

Colliding path

Local path

c

c

c

c’

c

c’


Probabilistic roadmap method1
Probabilistic Roadmap Method

Construction (G =V,E )

Loop

c a free sample

add c to the vertices V

Nc a set of nodes

for allc’ in Nc in increasing distance

ifc’ and c are not connected in Gthen

if local path between c and c’ exists then

add the edge c’c to E

Query

connect sample s and g to roadmap

Dijkstra’s shortest path

Free space

Forbidden space

Sample

Local path

Start / goal

Shortest path


Methods
Methods

  • General setup

    • SAMPLE

      • Implemented in C++ using VS.NET 2003

      • Easy API to add techniques

      • GUI: easily set up experiments

      • Repeatability: load/save an experiment

      • Easily comparing different techniques

      • Easily examining parameter of a technique

      • Automatically collect/process data of experiment

    • Demo


Methods1
Methods

  • Test problems

    • Conclusions were often too general due to limited set of problems

    • Also choose worst-case problems


Methods2
Methods

  • Interchangeability

    • Libraries taking take of common functionality

      • Collision checking, visualization

        Callisto:http://www.cs.uu.nl/dennis/callisto/callisto.html[Nieuwenhuisen]

      • Graph utilities

        Atlas: http://www.cs.uu.nl/dennis/atlas/atlas.html[Nieuwenhuisen]

      • Nearest neighbor

        MPNN: http://msl.cs.uiuc.edu/~yershova/mpnn/mpnn.htm[Yershova, Lavalle]

      • Deterministic sampling methods

        http://msl.cs.uiuc.edu/~yershova/so3sampling/so3sampling.htm[Yershova]

      • Rotation in 3D

        http://www.kuffner.org/james/software [Kuffner]


Methods3
Methods

  • Interchangeability

    • Source code of motion planning framework

      • Motion planning kit

        MPK: http://ai.stanford.edu/~mitul/mpk [Latombe]

      • Move3D

        http://www.laas.fr/~nic/Move3D[Siméon]

      • Motion strategy library

        MSL: http://msl.cs.uiuc.edu/msl [Lavalle]

    • Unfortunately, code is often not up-to-date


Methods4
Methods

  • Interchangeability

    • Sources

      • Geometry of environment/robot: VRML

      • Problem descriptions: XML

    • Advantages of using existing languages

      • Well documented

      • Parsers/type checkers are available for all platforms

      • Existing programs for creating/editing the files


Methods5
Methods

  • Interchangeability

    • Sources of geometry files and benchmarks

      • http://www.give-lab.cs.uu.nl/movie/moviemodels [MOVIE]

      • http://faculty.cs.tamu.edu/amato/dsmft/benchmarks[Amato]

      • http://mpb.ce.unipr.it/[Reggiani]

    • Problems should be put online when article is published


Results
Results

  • Evaluation of solution

    • Compare new technique with existing ones

      • Pitfall: parameter tuning only for the new technique

    • Compare against optimal solution

      • Often only known for trivial cases

      • Approximate optimal solution by many runs

    • User studies


Results1
Results

  • Statistics

    • Large variances in running times

      • Complicates statistical analysis

      • Makes analysis unreliable

      • Is undesirable from a user’s point of view

    • Perform large number of runs

    • Provide more statistical info, e.g. box plots

    • Deterministic versus randomized techniques

      • Deterministic techniques can respond sensitively to small changes in the problem setting


Conclusion
Conclusion

  • Automate conducting experiments as much as possible

  • Choose test problems carefully

  • Source code, software components and problem data should be made available

  • Use standard file formats (VRML, XML)

  • Provide an extensive statistical analysis


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