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Project: pyOptimalMotionPlanning (GitHub Link)

• pyOptimalMotionPlanning-master
  • viewsummary.py
  • pomp
    • visualizer.py
    • structures
      • randomdict.py
      • kdtree.py
      • __init__.py
      • nearestneighbors.py
      • knn.py
    • planners
      • kinodynamicplanner.py
      • optimization.py
      • rrtstarplanner.py
      • profiler.py
      • problem.py
      • allplanners.py
      • __init__.py
      • test.py
      • helpers.py
    • example_problems
      • flappy.py
      • geometric.py
      • dubins.py
      • pendulum.py
      • doubleintegrator.py
      • __init__.py
      • lqr.py
    • __init__.py
    • spaces
      • metric.py
      • sampler.py
      • se3space.py
      • edgechecker.py
      • objectives.py
      • interpolators.py
      • sampling.py
      • timespace.py
      • costspace.py
      • biassets.py
      • sets.py
      • configurationspace.py
      • statespace.py
      • so2space.py
      • differences.py
      • __init__.py
      • controlspace.py
      • geodesicspace.py
      • objective.py
    • klampt
      • trajectory.py
      • rootfind.py
      • se3.py
      • multipath.py
      • motionplanning.py
      • ik.py
      • robotcspace.py
      • so3.py
      • contactcspace.py
      • cspaceutils.py
      • robotcollide.py
      • glprogram.py
      • qtprogram.py
      • contact.py
      • hold.py
      • robotsim.py
      • camera.py
      • batch.py
      • so2.py
      • cspace.py
      • __init__.py
      • vectorops.py
      • resource.py
      • gldraw.py
      • loader.py
      • map.py
  • LICENSE
  • main.py
  • README.md
  • processresults.py
  • .gitignore

pyOptimalMotionPlanning

Optimal Motion Planning package in Python

v0.2

Kris Hauser

• v0.1: released 7/27/2015
• v0.2: released 12/24/2019. Added Python 3 support, some experimental numerical optimization libraries.

Requirements:

• Python 2.X or 3.X
• Numpy/Scipy
• PyOpenGL
• Matplotlib (optional)

Usage:

"python main.py [-v] [PROBLEM] [PLANNER]"

where -v is an optional flag to show the planner in a GUI. If not specified, the planner is run 10 times on the given problem and stats are saved to disk. You can also name multiple planners.

[PLANNER] can be any of

• "r-rrt"
• "r-est"
• "r-rrt-prune"
• "r-est-prune"
• "ao-rrt"
• "ao-est"
• "stable-sparse-rrt"
• "sst*"
• "all": run all planners You may also add keyword arguments to change parameters of the planner, e.g. "r-rrt(numControlSamples=1)".

[PROBLEM] can be any of:

• "Bugtrap"
• "Kink"
• "Pendulum"
• "Dubins"
• "Dubins2"
• "DoubleIntegrator"
• "Flappy"

Visualization controls:

• 'p' will do 1000 iterations of planning
• ' ' will do a single iteration.
• 'm' will start saving frames to disk every 100 iterations of planning, which can be later turned into a movie.

Once data has been saved to disk, you can run:

"python processresults.py [folder]"

to generate a csv file summarizing all of the results for a single problem. If you have matplotlib, you can then view the results using

"python viewsummary [file]"