ロボット計画のための構成空間分解によるスケーラブルな代理衝突判定
Configuration Space Decomposition for Scalable Proxy Collision Checking in Robot Planning and Control
順運動学空間でK-Meansクラスタリングにより構成空間を分割し、各サブ空間にFastronを学習させることで、高速かつ高精度な代理衝突判定を実現した論文。7自由度Baxterで平均29倍高速な衝突判定と最大9.8倍高速な動作計画を達成。
著者: Mrinal Verghese, Nikhil Das, Yuheng Zhi, Michael Yip
分類: cs.RO
原文アブストラクト
Real-time robot motion planning in complex high-dimensional environments remains an open problem. Motion planning algorithms, and their underlying collision checkers, are crucial to any robot control stack. Collision checking takes up a large portion of the computational time in robot motion planning. Existing collision checkers make trade-offs between speed and accuracy and scale poorly to high-dimensional, complex environments. We present a novel space decomposition method using K-Means clustering in the Forward Kinematics space to accelerate proxy collision checking. We train individual configuration space models using Fastron, a kernel perceptron algorithm, on these decomposed subspaces, yielding compact yet highly accurate models that can be queried rapidly and scale better to more complex environments. We demonstrate this new method, called Decomposed Fast Perceptron (D-Fastron), on the 7-DOF Baxter robot producing on average 29x faster collision checks and up to 9.8x faster motion planning compared to state-of-the-art geometric collision checkers.
