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