未知・動的環境における安全保証付きリアクティブポテンシャル場ナビゲーション
SafeGuardPF: Safety Guaranteed Reactive Potential Fields for Mobile Robots in Unknown and Dynamic Environments
リアクティブポテンシャル場による移動ロボットの衝突回避制御器をハイブリッドオートマトンとして定式化し、微分動的論理で受動的安全性を形式的に検証した。
著者: Rafael Rodrigues da Silva, Samuel Silva, Grigoriy Dubrovskiy, Hai Lin
分類: cs.RO, cs.MA, cs.SY, eess.SY
原文アブストラクト
An autonomous navigation with proven collision avoidance in unknown and dynamic environments is still a challenge, particularly when there are moving obstacles. A popular approach to collision avoidance in the face of moving obstacles is based on model predictive algorithms, which, however, may be computationally expensive. Hence, we adopt a reactive potential field approach here. At every cycle, the proposed approach requires only current robot states relative to the closest obstacle point to find the potential field in the current position; thus, it is more computationally efficient and more suitable to scale up for multiple agent scenarios. Our main contribution here is to write the reactive potential field based motion controller as a hybrid automaton, and then formally verify its safety using differential dynamic logic. In particular, we can guarantee a passive safety property, which means that collisions cannot occur if the robot is to blame, namely a collision can occur only if the robot is at rest. The proposed controller and verification results are demonstrated via simulations and implementation on a Pioneer P3-AT robot.
