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 Use randomly exploring trees Build trees from both start and finish nodes Path is found when the two trees connect

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

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

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

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

From Kuffner’s website

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