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Rapidly Exploring Random Trees for Path Planning: RRT-Connect

Rapidly Exploring Random Trees for Path Planning: RRT-Connect. Dave Lattanzi. Background. “Complete” algorithms ( Dijkstra ) are slow especially in higher dimensional configuration space Kuffner and LaValle proposed using randomized methods Published in IEEE, 2000. Basic Concept.

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Rapidly Exploring Random Trees for Path Planning: RRT-Connect

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  1. Rapidly Exploring Random Trees for Path Planning: RRT-Connect Dave Lattanzi

  2. Background • “Complete” algorithms (Dijkstra) are slow • especially in higher dimensional configuration space • Kuffner and LaValle proposed using randomized methods • Published in IEEE, 2000

  3. Basic Concept • Use randomly exploring trees • Build trees from both start and finish nodes • Path is found when the two trees connect

  4. Building a Random Tree • Tree building = graph building • Start the tree at a given node • Pick a random node in the graph of the map • Find the nearest node in the tree • Extend from nearest node by steps towards random node as long as possible • Add the new edge and vertex to the tree

  5. Random Tree Pseudocode Def BuildTree(start_node, Nnodes, Δstep): Initialize(Tree, start_node) For i = 1 to Nnodes: Get(random_node) nearest_node = Nearest(random_node, tree) new_node = Extend(nearest_node, Δstep) Tree.add(new_node) Return Tree

  6. RRT-Connect • Build a tree from start and end nodes • Path is found when two trees meet

  7. RRT Connect Pseudocode Def RRT(start_node, end_node, K (total nodes in map), Δstep): Initialize(startTree, start_node) Initialize(endTree, end_node) for i = 1 to K: ExtendTree(startTree, Δstep) ExtendTree(endTree, Δstep) if Connect(startTree,endTree) = True: Return Path

  8. From Kuffner’s website

  9. Advantages and Disadvantages • Fast! • Will always find a path if possible • No parameter tuning But: • Computational time is highly variable • Path is not repeatable or predictable • Lots of hunting for nearest neighbors in big lists

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