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We present CARRT∗ (Cache-Aware Rapidly Exploring Random Tree∗), an asymptotically optimal sampling-based motion planner that significantly reduces motion planning computation time by effectively utilizing the cache memory hierarchy of modern central processing units (CPUs). CARRT∗ can account for the CPU's cache size in a manner that keeps its working dataset in the cache. The motion planner progressively...
We present Parallel Rapidly Exploring Random Tree (PRRT) and Parallel RRT $^\ast$ (PRRT$^\ast$ ), which are sampling-based methods for feasible and optimal motion planning designed for modern multicore CPUs. We parallelize RRT and RRT$^\ast$ such that all threads concurrently build a single-motion planning tree. Parallelization in this manner requires data structures, such as the nearest neighbor...
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