衝突確率の上限を保証するシナリオベース運動計画
Scenario-Based Motion Planning with Bounded Probability of Collision
シナリオ最適化と予測制御を組み合わせ、計画全体にわたる全障害物との同時衝突確率を制約するリアルタイム可能なSafe Horizon MPCを提案し、移動ロボットと自動運転車で検証した。
著者: Oscar de Groot, Laura Ferranti, Dariu Gavrila, Javier Alonso-Mora
分類: cs.RO
原文アブストラクト
Robots will increasingly operate near humans that introduce uncertainties in the motion planning problem due to their complex nature. Typically, chance constraints are introduced in the planner to optimize performance while guaranteeing probabilistic safety. However, existing methods do not consider the actual probability of collision for the planned trajectory, but rather its marginalization, that is, the independent collision probabilities for each planning step and/or dynamic obstacle, resulting in conservative trajectories. To address this issue, we introduce a novel real-time capable method termed Safe Horizon MPC, that explicitly constrains the joint probability of collision with all obstacles over the duration of the motion plan. This is achieved by reformulating the chance-constrained planning problem using scenario optimization and predictive control. Our method is less conservative than state-of-the-art approaches, applicable to arbitrary probability distributions of the obstacles' trajectories, computationally tractable and scalable. We demonstrate our proposed approach using a mobile robot and an autonomous vehicle in an environment shared with humans.
