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Multiboat Trajectory Optimization

shapeshifting branch

Trajectory Planning for the Shapeshifting of Autonomous Surface Vessels

Gheneti, Banti; Park, Shinkyu; Kelly, Ryan; Meyers, Drew; Leoni, Pietro; Ratti, Carlo; Rus, Daniela L

  • c-space computation for a vessel made of rectangles moving around another vessel made of rectangles, using shapely
  • trajectory optimization algorithm for solving collision free trajectories to shapeshift in the c-space, using pydrake
  • also includes features in the master branch described below

presented at the International Symposium on Multi-Robot and Multi-Agent Systems (IEEE-MRS 2019)

master branch

Multiboat minimum makespan formation planning in three stages.

  • Goal assignment with the Hungarian algorithm with initial state to goal high order norm costs
  • Linear and shape-based interpolation for trajectory initialization
  • Direct transcription trajectory optimization using pydrake with SNOPT SQP solver

Final project for 6.832 - Underactuated Robotics

See Youtube for an explanation video.

Contents

  • .py files include code for the above 3 stages, as well as for producing visualizations
  • final_project_visualizations.ipynb contains code for running experiments on the .py files and saving them to /results
  • final_project_visualizations.experiments for viewing result files as tables, displaying and saving boat animations, and plotting various graphs.
  • /results contains experiment results
  • /animations contains various experiment animations
  • /icp contains the iterative closest point (ICP) implementation obtained from @ClayFlanigan.

Dependencies

  • Main dependency is drake
  • Install other python dependencies with pip install sklearn tabular

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