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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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