Distributed Coordination and Data Fusion for Multi-robot Search

This paper presents coordination and data fusion methods for teams of vehicles performing target search tasks without guaranteed communication. A fully distributed team planning algorithm is proposed that utilizes limited shared information as it becomes available, and data fusion techniques are introduced for merging estimates of the target’s position from vehicles that regain contact after long periods of time. The proposed data fusion techniques are shown to avoid overcounting information, which ensures that combining data from different vehicles will not decrease the performance of the search. Motivated by the underwater search domain, a realistic underwater acoustic communication channel is used to determine the probability of successful data transfer between two locations. The channel model is validated using acoustic communication data and integrated into a simulation of multiple autonomous vehicles in both open ocean and harbor environments. The results demonstrate that distributed coordination with limited communication significantly improves team performance versus prior techniques that continually maintain connectivity.