局所解からの脱出：壁沿い移動を組み合わせた分散型マルチロボットナビゲーションのためのハイブリッド人工ポテンシャル場
Escaping Local Minima: Hybrid Artificial Potential Field with Wall-Follower for Decentralized Multi-Robot Navigation
地図や通信なしで局所センサ情報のみを用い、人工ポテンシャル場と壁沿い移動を切り替えることで、非凸障害物環境でもロボットが局所解に陥らず衝突回避しながら目的地へ到達できる分散ナビゲーション手法を提案した。
著者: Joonkyung Kim, Sangjin Park, Wonjong Lee, Woojun Kim, Nakju Doh, Changjoo Nam
分類: cs.RO
原文アブストラクト
We tackle the challenges of decentralized multi-robot navigation in environments with nonconvex obstacles, where complete environmental knowledge is unavailable. While reactive methods like Artificial Potential Field (APF) offer simplicity and efficiency, they suffer from local minima, causing robots to become trapped due to their lack of global environmental awareness. Other existing solutions either rely on inter-robot communication, are limited to single-robot scenarios, or struggle to overcome nonconvex obstacles effectively. Our proposed methods enable collision-free navigation using only local sensor and state information without a map. By incorporating a wall-following (WF) behavior into the APF approach, our method allows robots to escape local minima, even in the presence of nonconvex and dynamic obstacles including other robots. We introduce two algorithms for switching between APF and WF: a rule-based system and an encoder network trained on expert demonstrations. Experimental results show that our approach achieves substantially higher success rates compared to state-of-the-art methods, highlighting its ability to overcome the limitations of local minima in complex environments
