发表机构
Massachusetts Institute of Technology; Charles Stark Draper Laboratory（麻省理工学院; 查尔斯·斯塔克·德拉珀实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出MACE分散式探索框架，通过自适应通信窗口决策，在间歇通信下平衡信息共享与探索成本，相比现有策略减少总探索时间高达23%。
AI 中文摘要
使用多机器人团队探索未知环境可以通过允许机器人并行探索来提高效率。然而，实现这些收益需要有效的信息共享。当通信间歇性发生时，机器人必须平衡共享信息的收益与偏离探索以建立通信的成本。本文介绍了MACE，一种分散式探索框架，该框架主动评估建立通信是否值得。在预定的通信窗口，机器人估计到达先前确定的通信地点的成本。通过将此决策表述为车辆定向问题的一种变体，机器人根据建立通信所需的行程以及沿途可完成的探索来评估路线。这种方法使机器人能够比纯机会主义策略更频繁地通信，同时减少与固定会合策略相关的不必要行程。在一组具有不同大小和几何形状的模拟环境中，我们证明与现有的通信受限探索策略相比，MACE将总探索时间减少了高达23%。
英文摘要
Exploring unknown environments with multi-robot teams can improve efficiency by allowing robots to explore in parallel. However, realizing these gains requires effective information sharing. When communication is intermittent, robots must balance the benefits of sharing information against the cost of diverting from exploration to establish communication. This paper introduces MACE, a decentralized exploration framework that actively evaluates whether establishing communication is worthwhile. At scheduled communication windows, robots estimate the cost of reaching previously identified communication locations. By formulating this decision as a variant of the Vehicle Orienteering Problem, robots evaluate routes based on the travel required to establish communication and the exploration that can be completed along the way. This approach enables robots to communicate more frequently than under purely opportunistic strategies while reducing the unnecessary travel associated with fixed rendezvous strategies. Across a set of simulated environments with varying size and geometry, we demonstrate that MACE reduces the total exploration time by up to 23% compared to existing communication-constrained exploration strategies.
Comments8 pages, 6 figures
