Multi-robot teams improve exploration by smart communication timing
Communication-Constrained Multi-Robot Exploration With Adaptive Communication Windows
Robotics
Summary
Exploring unknown areas with many robots can be faster, but robots need to share what they find. Sharing information takes time and effort, especially when communication isn’t always available. The authors created a system called MACE that helps robots decide the best times to connect and share without wasting time traveling just to meet. This approach made exploration up to 23% faster in computer simulations compared to older methods.
What this means in practice
- •For robotics engineers: Coordinate multiple robots in environments with limited communication by scheduling cost-effective meeting times to share discoveries.
- •For search and rescue teams: Enhance robot team deployment in disaster areas by reducing exploration time through smarter communication management.
Authors
Ben Rossano, Jaein Lim, Jonathan P. How
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.