1 / 9

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.

thao
Download Presentation

Rapidly Exploring Random Trees for Path Planning: RRT-Connect

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related