A scalable distributed RRT for motion planning

Sam Ade Jacobs, Nicholas Stradford, Cesar Rodriguez, Shawna Thomas, Nancy M. Amato
2013 2013 IEEE International Conference on Robotics and Automation  
Rapidly-exploring Random Tree (RRT), like other sampling-based motion planning methods, has been very successful in solving motion planning problems. Even so, samplingbased planners cannot solve all problems of interest efficiently, so attention is increasingly turning to parallelizing them. However, one challenge in parallelizing RRT is the global computation and communication overhead of nearest neighbor search, a key operation in RRTs. This is a critical issue as it limits the scalability of
more » ... previous algorithms. We present two parallel algorithms to address this problem. The first algorithm extends existing work by introducing a parameter that adjusts how much local computation is done before a global update. The second algorithm radially subdivides the configuration space into regions, constructs a portion of the tree in each region in parallel, and connects the subtrees, removing cycles if they exist. By subdividing the space, we increase computation locality enabling a scalable result. We show that our approaches are scalable. We present results demonstrating almost linear scaling to hundreds of processors on a Linux cluster and a Cray XE6 machine.
doi:10.1109/icra.2013.6631304 dblp:conf/icra/JacobsSRTA13 fatcat:pfyvw6n6bbbrxkv26o2za46viu