時間不確実性下における複数ロボットの観光案内のための人間-ロボット同時マッチングと経路計画
Simultaneous Human-robot Matching and Routing for Multi-robot Tour Guiding under Time Uncertainty
博物館のような不確実性を含む環境で、人間の興味地点に基づきロボットガイドを割り当てつつ経路とスケジュールを同時に計画する集中型マルチロボット計画手法を提案し、大規模シミュレーションで拡張性と頑健性を検証した。
著者: Bo Fu, Tribhi Kathuria, Denise Rizzo, Matthew Castanier, X. Jessie Yang, Maani Ghaffari, Kira Barton
分類: cs.RO, cs.MA
原文アブストラクト
This work presents a framework for multi-robot tour guidance in a partially known environment with uncertainty, such as a museum. In the proposed centralized multi-robot planner, a simultaneous matching and routing problem (SMRP) is formulated to match the humans with robot guides according to their selected places of interest (POIs) and generate the routes and schedules for the robots according to uncertain spatial and time estimation. A large neighborhood search algorithm is developed to efficiently find sub-optimal low-cost solutions for the SMRP. The scalability and optimality of the multi-robot planner are evaluated computationally under different numbers of humans, robots, and POIs. The largest case tested involves 50 robots, 250 humans, and 50 POIs. Then, a photo-realistic multi-robot simulation platform was developed based on Habitat-AI to verify the tour guiding performance in an uncertain indoor environment. Results demonstrate that the proposed centralized tour planner is scalable, makes a smooth trade-off in the plans under different environmental constraints, and can lead to robust performance with inaccurate uncertainty estimations (within a certain margin).
