Communication-Constrained Multi-Robot Exploration With Adaptive Communication Windows

2026-09-11

AI Summary

该研究解决了多机器人在通信受限环境下探索未知环境的问题，通过引入MACE框架主动评估建立通信的成本与收益，优化了探索效率。

Abstract

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.

Problem

• Communication-Constrained
• Multi-Robot Exploration
• Information Sharing
• Intermittent Communication

Innovation

• Decentralized Exploration
• Adaptive Communication Windows
• Vehicle Orienteering Problem