Distributed Estimation for Motion Coordination in an Unknown Spatiotemporal Flowfield

Cooperating autonomous vehicles perform better than uncooperating vehicles for applications such as surveillance, environmental sampling and target tracking. For multiple vehicles to cooperate effectively, the navigation control laws should account for disturbances caused by ocean currents or atmospheric winds. This paper provides dynamic decentralized control algorithms for motion coordination in an unknown, time-invariant flowfield. The algorithms simultaneously estimate the flowfield and use that estimate in an observer-based feedback control that stabilizes a moving formation. Each vehicle uses noisy measurements of its own position to generate independent flowfield estimates. For a uniform flowfield, we provide a theoretically justified approach for each vehicle to estimate the flow independently. For a nonuniform flowfield, we propose a distributed algorithm using an information filter to reconstruct the flowfield and a consensus filter to share information between vehicles. In either case, the vehicles use the flowfield estimate to steer to a circular formation.