A source-seeking strategy for an autonomous underwater vehicle via on-line field estimation

This paper studies the problem of using an autonomous underwater vehicle (AUV) to seek the source of some signal in the underwater environment. To avoid the redundant travels for getting the local gradient in existing methods, we propose a novel source-seeking strategy in which the AUV keeps updating an estimated field model using measurements along its gradient-climbing path. Based on the convergence results of the sequential least-squares field estimation algorithm and the path planning method by nonlinear programming, the AUV will finally reach a maximum of the field which indicates a potential source. The way-point tracking control of the AUV is realized by a nonlinear model predictive control (NMPC) scheme embedded with the line-of-sight (LOS) guidance law. The effectiveness and efficiency of the proposed source-seeking strategy is validated in the simulation experiment with a real AUV model.