SOURCE-LINKED INTELLIGENCE
Communication-Constrained Multi-Robot Exploration With Adaptive Communication Windows
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 communi
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-11T07:05:42.000Z
First collected: 2026-09-20T18:22:04.777Z. This is not the publication date.