多様体制約付き動作計画における投影のベクトル化による実時間全身制御
Vectorizing Projection in Manifold-Constrained Motion Planning for Real-Time Whole-Body Control
多様体制約付き動作計画をCPUのSIMD並列化で高速化し、従来比100〜1000倍の速度向上を実現。実ヒューマノイドロボットでの実時間全身計画を実証した。
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著者: Shrutheesh R Iyer, I-Chia Chang, Andrew Z. Liu, Yan Gu, Zachary Kingston
分類: cs.RO
原文アブストラクト
Many robot planning tasks require satisfaction of one or more constraints throughout the entire trajectory. For geometric constraints, manifold-constrained motion planning algorithms are capable of planning collision-free path between start and goal configurations on the constraint submanifolds specified by task. Current state-of-the-art methods can take tens of seconds to solve these tasks for complex systems such as humanoid robots, making real-world use impractical, especially in dynamic settings. Inspired by recent advances in hardware accelerated motion planning, we present a CPU SIMD-accelerated manifold-constrained motion planner that revisits projection-based constraint satisfaction through the lens of parallelization. By transforming relevant components into parallelizable structures, we use SIMD parallelism to plan constraint satisfying solutions. Our approach achieves up to 100-1000x speed-ups over the state-of-the-art, making real-time constrained motion planning feasible for the first time. We demonstrate our planner on a real humanoid robot and show real-time whole-body quasi-static plan generation. Our work is available at https://commalab.org/papers/mcvamp/.
