Path Planning & Smoothing

Classical motion planning algorithms and trajectory refinement

Context

Implemented core motion planning and path smoothing algorithms to build solid intuition for geometric planning foundations.

My Contribution

  • Implemented sampling-based planners (RRT, RRT*) and ran them via a simple main.py entrypoint
  • Designed path post-processing / smoothing utilities and evaluated smoothness qualitatively via visualizations
  • Added config-driven experiments (planner choice, 2D/3D, domain size, iterations, step size, obstacles, visualization)

Technical Details

Algorithms explored:

  • Sampling-based planning (RRT, RRT*)
  • Path interpolation, curvature reduction, collision checking (as part of the broader exploration focus)

Focus was bridging theoretical planning concepts with practical implementation and visualization.

  • https://github.com/bxtbold/path_planning
  • https://github.com/bxtbold/path_smoothing