MR. POP: ほぼ確実に漸近最適なマルチロボット並列最適化プランナ
MR. POP: Multi-Robot Parallel Optimizing Planner for Almost-Surely Asymptotically Optimal Planning
GPUの大規模並列性を活用し、dRRTとAO-xを基盤としたマルチロボット運動計画を高速化する手法を提案。最大35自由度の系で100%の解決率を達成し、下流の最適化の成功率も大幅に向上させた。
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著者: Chih H. Huang, Roy Xing, Brian Plancher, Zachary Kingston
分類: cs.RO
原文アブストラクト
Finding globally optimal paths remains a fundamental challenge in multi-robot motion planning. Despite acceleration of almost-surely asymptotically optimal (a.s.a.o.) planners via CPU-based parallelism, achieving both probabilistic convergence guarantees and strong computational performance, these algorithms still struggle to scale to multi-robot settings. As such, we introduce MR. POP, a GPU-based a.s.a.o. multi-robot planner based on dRRT and the AO-x meta-algorithm. MR. POP uses large-scale GPU-based SIMT-parallelism to simultaneously run hundreds of roadmap construction and tree search iterations with underlying parallel nearest neighbor search and collision checking operations. We show that this enables MR. POP to become the only planner achieving a 100% solve rate while being faster than state-of-the-art a.s.a.o. planners in multi-robot systems up to 35-DOF. MR. POP also raises the success rate of downstream motion optimizers (e.g., from 4% to 72%), by creating high-quality, diverse seeds that help avoid local minima.