動的環境における高自由度ロボットの高速・誤差上限付き確率的衝突検出
Fast and Bounded Probabilistic Collision Detection in Dynamic Environments for High-DOF Trajectory Planning
動的で不確実な環境で高自由度ロボットと障害物の衝突確率を誤差上限付きで高速に計算し、将来の障害物位置・運動予測と組み合わせた軌道最適化を実現した。
著者: Chonhyon Park, Jae Sung Park, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present a novel approach to perform probabilistic collision detection between a high-DOF robot and high-DOF obstacles in dynamic, uncertain environments. In dynamic environments with a high-DOF robot and moving obstacles, our approach efficiently computes accurate collision probability between the robot and obstacles with upper error bounds. Furthermore, we describe a prediction algorithm for future obstacle position and motion that accounts for both spatial and temporal uncertainties. We present a trajectory optimization algorithm for high-DOF robots in dynamic, uncertain environments based on probabilistic collision detection. We highlight motion planning performance in challenging scenarios with robot arms operating in environments with dynamically moving human obstacles.
