时间不确定性下多机器人导游中的人-机器人同时匹配与路径选择
Simultaneous Human-robot Matching and Routing for Multi-robot Tour Guiding under Time Uncertainty
摘要：
本文提出了一种在部分已知的不确定环境中（如博物馆）进行多机器人导游的框架。在建议的集中式多机器人规划器，同时匹配和路由问题（SMRP）制定匹配的人与机器人导游根据他们选择的兴趣点（POI），并根据不确定的空间和时间的估计产生的机器人的路线和时间表。一个大的邻域搜索算法，有效地找到次优的低成本解决方案的SMRP。的可扩展性和最优性的多机器人规划计算评估下不同数量的人，机器人和POI。测试的最大案例涉及50个机器人，250个人类和50个POI。为了验证该系统在不确定室内环境中的导游性能，基于人工智能开发了一个具有照片级真实感的多机器人仿真平台。结果表明，所提出的集中式旅游规划是可扩展的，在不同的环境约束下的计划，使一个平稳的权衡，并可以导致鲁棒性能与不准确的不确定性估计（在一定的利润率）。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
