RPF-Search: 未知の動的環境におけるロボット人物追従のための場ベース探索
RPF-Search: Field-based Search for Robot Person Following in Unknown Dynamic Environments
未知の動的環境で人物追従ロボットが遮蔽物により目標を見失う問題に対し、地形遮蔽と動的遮蔽に別々に対処するヒューリスティック探索フレームワークを提案し、シミュレーションと実機で探索効率と成功率を向上させた。
著者: Hanjing Ye, Kuanqi Cai, Yu Zhan, Bingyi Xia, Arash Ajoudani, Hong Zhang
分類: cs.RO
原文アブストラクト
Autonomous robot person-following (RPF) systems are crucial for personal assistance and security but suffer from target loss due to occlusions in dynamic, unknown environments. Current methods rely on pre-built maps and assume static environments, limiting their effectiveness in real-world settings. There is a critical gap in re-finding targets under topographic (e.g., walls, corners) and dynamic (e.g., moving pedestrians) occlusions. In this paper, we propose a novel heuristic-guided search framework that dynamically builds environmental maps while following the target and explicitly addresses these two types of occlusions through distinct mechanisms. For topographic occlusions, a belief-guided search field estimates the likelihood of the target's presence and guides search toward promising frontiers. For dynamic occlusions, an observation-based search strategy adaptively switches between a fluid-following field and an overtaking potential field based on occluder motion patterns. Our results demonstrate that the proposed method outperforms existing approaches in terms of search efficiency and success rates, both in simulations and real-world tests. Our target search method enhances the adaptability and reliability of RPF systems in unknown and dynamic environments, supporting their use in real-world applications.
