An Approximate Set Membership Approach to Resilient Multi-Robot Communication

Abstract

Effective communication is critical for coordinating multi-robot teams, yet practical deployments often face severe bandwidth constraints and frequent message loss. This paper presents a communication protocol that leverages Bloom filters to enable efficient, approximate set membership queries in multi-robot systems. Bloom filters offer a tunable trade-off between false positive rate and memory footprint, making them well suited for bandwidth-limited communication. To mitigate the effects of false positives, we introduce a salting strategy that decorrelates Bloom filters and enables stacking - the combination of membership queries across multiple filters. These stacked results are incorporated into each robot's belief map, such that only sufficiently corroborated information influences frontier generation and exploration planning. We evaluate our proposed communication protocol in a multi-robot exploration task, where robots share information about their observed cells to enable efficient coverage. Our results demonstrate that compared to exact methods, our Bloom filter-based protocol reduces communication cost by up to 6 times while maintaining team exploration performance, even under severe communication dropouts.