不確実な動的環境における確率制約付き衝突回避のための確率的リスク評価
Probabilistic Risk Assessment for Chance-Constrained Collision Avoidance in Uncertain Dynamic Environments
複数の確率的プランナを並列実行し、各計画のリスクをリアルタイム評価して、指定閾値を満たす中で最も保守的でない計画を選ぶ手法を提案。移動ロボットと人間が共存する環境での実験で有効性を示した。
著者: Khaled A. Mustafa, Oscar de Groot, Xinwei Wang, Jens Kober, Javier Alonso-Mora
分類: cs.RO
原文アブストラクト
Balancing safety and efficiency when planning in crowded scenarios with uncertain dynamics is challenging where it is imperative to accomplish the robot's mission without incurring any safety violations. Typically, chance constraints are incorporated into the planning problem to provide probabilistic safety guarantees by imposing an upper bound on the collision probability of the planned trajectory. Yet, this results in overly conservative behavior on the grounds that the gap between the obtained risk and the specified upper limit is not explicitly restricted. To address this issue, we propose a real-time capable approach to quantify the risk associated with planned trajectories obtained from multiple probabilistic planners, running in parallel, with different upper bounds of the acceptable risk level. Based on the evaluated risk, the least conservative plan is selected provided that its associated risk is below a specified threshold. In such a way, the proposed approach provides probabilistic safety guarantees by attaining a closer bound to the specified risk, while being applicable to generic uncertainties of moving obstacles. We demonstrate the efficiency of our proposed approach, by improving the performance of a state-of-the-art probabilistic planner, in simulations and experiments using a mobile robot in an environment shared with humans.
