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Simple implementation of a Rapidly-exploring Random Tree (RRT) algorithm for path planning.

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Rapidly-Exploring Random Tree (RRT)

About

Path-planning is a resource-intensive task for robots / UAVs. The RRT algorithm provides [1] a way to solve this problem. The implementation is simple (see also [2]), but it comes with a cost...the path generated from the starting point till the goal is not optimal, usually post-processing is required.

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Red dots - obstacles; Yellow dots - start/goal point locations; Pink dots - generated path.

How to use

The RTT class has the following parameter on instantiation:

STATE_SPACE - define the limits of the search area;
GOAL_STOP_RADIUS - distance to stop, nearby the goal;
LINE_SIZE - length of exploring line (lover values => higher precision => higher resource consumption);
COLL_DIST - safe distance, to avoid collisions with obstacles;
MAP - numpy array [n,2] array, holds the obstacles;

Execute the 'test_rrt.py' to see the RRT in action.

Resources

  1. Rapidly-Exploring Random Tree
  2. simpleAstar

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Simple implementation of a Rapidly-exploring Random Tree (RRT) algorithm for path planning.

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