ガウス不確かさ下の運動計画における厳密かつ有界な衝突確率
Exact and Bounded Collision Probability for Motion Planning under Gaussian Uncertainty
ロボットと障害物を楕円体で近似し、ガウス分布の運動・センシング不確かさ下での衝突確率を厳密に計算する手法と、オンライン計画向けの高速な上界を提案した論文。
著者: Antony Thomas, Fulvio Mastrogiovanni, Marco Baglietto
分類: cs.RO, cs.AI
原文アブストラクト
Computing collision-free trajectories is of prime importance for safe navigation. We present an approach for computing the collision probability under Gaussian distributed motion and sensing uncertainty with the robot and static obstacle shapes approximated as ellipsoids. The collision condition is formulated as the distance between ellipsoids and unlike previous approaches we provide a method for computing the exact collision probability. Furthermore, we provide a tight upper bound that can be computed much faster during online planning. Comparison to other state-of-the-art methods is also provided. The proposed method is evaluated in simulation under varying configuration and number of obstacles.
