距離場上の動作計画のための安全バブル被覆
Safe Bubble Cover for Motion Planning on Distance Fields
距離場の一点の距離値から安全領域（安全バブル）を求め、自由空間をバブルで被覆する3つのサンプリングアルゴリズムを提案し、階層的な計画で効率的な衝突回避軌道を生成する。
著者: Ki Myung Brian Lee, Zhirui Dai, Cedric Le Gentil, Lan Wu, Nikolay Atanasov, Teresa Vidal-Calleja
分類: cs.RO
原文アブストラクト
We consider the problem of planning collision-free trajectories on distance fields. Our key observation is that querying a distance field at one configuration reveals a region of safe space whose radius is given by the distance value, obviating the need for additional collision checking within the safe region. We refer to such regions as safe bubbles, and show that safe bubbles can be obtained from any Lipschitz-continuous safety constraint. Inspired by sampling-based planning algorithms, we present three algorithms for constructing a safe bubble cover of free space, named bubble roadmap (BRM), rapidly exploring bubble graph (RBG), and expansive bubble graph (EBG). The bubble sampling algorithms are combined with a hierarchical planning method that first computes a discrete path of bubbles, followed by a continuous path within the bubbles computed via convex optimization. Experimental results show that the bubble-based methods yield up to 5- 10 times cost reduction relative to conventional baselines while simultaneously reducing computational efforts by orders of magnitude.
