Distributed Estimation for Motion Coordination in an Unknown Spatially Varying Flowfield

The development of decentralized motion coordination algorithms in the presence of an unknown spatially varying flowfield was studied. It is assumed that each vehicle can measure or approximate the local flowfield at its current position. The measurements are incorporated into an information-consensus filter to estimate the parameters of a global spatially varying flowfield model. The authors provide a distributed motion coordination algorithm that estimates a spatially varying flowfield and uses the estimate in a closed-loop multivehicle control. Each vehicle implements an information-consensus filter to reconstruct the flowfield parameters from noisy position measurements. Simulations are provided to illustrate the performance of the estimated control algorithms in stabilizing a circular formation in a parameterized spatially variable flowfield. This approach is viable in an arbitrary, random flowfield.