Scalable Multicore Motion Planning Using Lock-Free Concurrency

We present PRRT (Parallel RRT) and PRRT* (Parallel RRT*), sampling-based methods for feasible and optimal motion planning designed for modern multicore CPUs. We parallelize RRT and RRT* such that all threads concurrently build a single motion planning tree. Parallelization in this manner requires that data structures, such as the nearest neighbor search tree and the motion planning tree, are safely shared across multiple threads. Rather than rely on traditional locks which can result in slowdowns due to lock contention, we introduce algorithms based on lock-free concurrency using atomic operations. We further improve scalability by using partition-based sampling (which shrinks each core's working data set to improve cache efficiency) and parallel work-saving (in reducing the number of rewiring steps performed in PRRT*). Because PRRT and PRRT* are CPU-based, they can be directly integrated with existing libraries. We demonstrate that PRRT and PRRT* scale well as core counts increase, in some cases exhibiting superlinear speedup, for scenarios such as the Alpha Puzzle and Cubicles scenarios and the Aldebaran Nao robot performing a 2-handed task.