Ben Rossano | Search arXiv

arXiv2026

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 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.

cs.RO↗

arXiv2026

Uncertainty-Aware Multi-Robot Task Allocation With Strongly Coupled Inter-Robot Rewards

Allocating tasks to heterogeneous robot teams in environments with uncertain task requirements is a fundamentally challenging problem. Redundantly assigning multiple robots to such tasks is overly conservative, while purely reactive strategies risk costly delays in task completion when the uncertain capabilities become necessary. This paper introduces an auction-based task allocation algorithm that explicitly models task requirement uncertainty, leveraging a novel strongly coupled formulation to allocate tasks such that robots with potentially required capabilities are naturally positioned near uncertain tasks. This approach enables robots to remain productive on nearby tasks while simultaneously mitigating large delays in completion time when their capabilities are required. Through a set of simulated disaster relief missions with task deadline constraints, we demonstrate that the proposed approach yields up to a 15% increase in expected mission value compared to redundancy-based methods. Furthermore, we propose a novel framework to approximate uncertainty arising from unmodeled changes in task requirements by leveraging the natural delay between encountering unexpected environmental conditions and confirming whether additional capabilities are required to complete a task. We show that our approach achieves up to an 18% increase in expected mission value using this framework compared to reactive methods that do not leverage this delay.

cs.RO↗