Collection of rrt-based algorithms that scale to n-dimensions:

• rrt
• rrt* (rrt-star)
• rrt* (bidirectional)
• rrt* (bidriectional, lazy shortening)
• rrt connect

Utilizes R-trees to improve performance by avoiding point-wise collision-checking and distance-checking.

This would not have been possible without Steven M. LaValle's excellent Planning Algorithms book, specifically Chapter 5, Section 5: Sampling-Based Motion Planning, Rapidly Exploring Dense Trees.

• Python 3+
• NumPy
• Rtree
• Plotly (only needed for plotting)

Define an n-dimensional Search Space, and n-dimensional obstacles within that space. Assign start and goal locations as well as the number of iterations to expand the tree before testing for connectivity with the goal, and the max number of overall iterations.

Assign bounds to Search Space in form: [(x_lower, x_upper), (y_lower, y_upper), ...]

Points represented by tuples of form: (x, y, ...)

Axis-aligned (hyper)rectangles represented by a tuples of form (x_lower, y_lower, ..., x_upper, y_upper, ...)

Non-axis aligned (hyper)rectangles or other obstacle representations should also work, provided that collision_free and obstacle_free are updated to work with the new obstacles.

Assign resolution of edges:

• q: Distance away from existing vertices to probe.
• r: Discretization length to use for edges when sampling along them to check for collisions. Higher numbers run faster, but may lead to undetected collisions.

Visualization examples can be found for rrt and rrt* in both 2 and 3 dimensions.

• 2D RRT
• 3D RRT
• 2D RRT*
• 3D RRT*
• 2D Bidirectional RRT*
• 3D Bidirectional RRT*
• 2D Heuristic Bidirectional RRT*
• 3D Heuristic Bidirectional RRT*

1. Fork it!
2. Create your feature branch: git checkout -b my-new-feature
3. Commit your changes: git commit -am 'Add some feature'
4. Push to the branch: git push origin my-new-feature
5. Submit a pull request :D

1. Steven Michael Lavalle. Planning Algorithms. New York (Ny), Cambridge University Press, 2014, pp. 228–237, lavalle.pl/planning/.
2. LaValle, Steven. "Rapidly-exploring random trees: A new tool for path planning." Research Report 9811 (1998).
3. Kuffner, James J., and Steven M. LaValle. "RRT-connect: An efficient approach to single-query path planning." Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065). Vol. 2. IEEE, 2000.

MIT License