论文标题
遍历主管问题：多机器人帮助的大约最佳方法
Traversing Supervisor Problem: An Approximately Optimal Approach to Multi-Robot Assistance
论文作者
论文摘要
多年来，部署在现场应用程序中的多机器人系统的数量已大大增加。尽管导航算法最近有所进步，但自主机器人经常遇到挑战性的情况，在控制政策失败并且需要人为援助才能恢复机器人任务。人类机器人的合作可以帮助实现高水平的自主权，但是单个人类主管立即监视和管理多个机器人仍然是一个具有挑战性的问题。我们的目标是帮助主管决定哪些机器人可以协助哪种命令，以最大程度地提高团队绩效。我们将不确定环境中的一对多监督问题制定为动态图形遍历问题。开发了基于静态图上有利可图的旅行问题的近似算法，以解决原始问题，并对近似误差进行界定和分析。我们对模拟自主农场的案例研究表明，在任务完成时间和人工工作时间内，与基线方法相比，我们的团队表现出色，并且我们的方法可以实时部署适量的机器人车队。
The number of multi-robot systems deployed in field applications has increased dramatically over the years. Despite the recent advancement of navigation algorithms, autonomous robots often encounter challenging situations where the control policy fails and the human assistance is required to resume robot tasks. Human-robot collaboration can help achieve high-levels of autonomy, but monitoring and managing multiple robots at once by a single human supervisor remains a challenging problem. Our goal is to help a supervisor decide which robots to assist in which order such that the team performance can be maximized. We formulate the one-to-many supervision problem in uncertain environments as a dynamic graph traversal problem. An approximation algorithm based on the profitable tour problem on a static graph is developed to solve the original problem, and the approximation error is bounded and analyzed. Our case study on a simulated autonomous farm demonstrates superior team performance than baseline methods in task completion time and human working time, and that our method can be deployed in real-time for robot fleets with moderate size.
