State Estimation and Compression Method for the Navigation of Multiple Autonomous Underwater Vehicles with Limited Communication Traffic

Abstract

This study proposes a state estimation and compression method for navigating multiple autonomous underwater vehicles (AUVs) toward wide area surveys near seafloors. In the proposed method, a moving AUV navigates by referencing a stationary landmark AUV on the seafloor. By alternating the landmark role, all AUVs can cover a wide area while maintaining low positioning errors. The moving AUV estimates the states (positions and headings) of both moving and landmark AUVs by a stochastic approach called a particle filter. When AUVs exchange their landmark roles, they must share their estimated states. However, state sharing is precluded by the low data rate of acoustical communications in underwater environments. To overcome the problem, this paper proposes a state compression method in which AUVs approximate their states by "particle clustering" based on a clustering method (k-means) and a model evaluation method (Akaike information criterion). The compression method enables AUVs to share their states by communicating small amounts of data. The proposed method was evaluated in simulations of two AUVs navigating over a 300 × 300-m2 seafloor area. Throughout the simulation, the proposed method maintained stable positioning and successful state sharing with small communication data size.