面向海洋环境自适应采样的多 AUV 协同定位

Efficient and accurate water quality monitoring is of great significance to the development of marine resources, and autonomous underwater vehicle (AUV) has broad application prospects in marine environmental monitoring. There are problems such as low efficiency, poor reliability, insufficient coverage and poor positioning accuracy when a single AUV performs water quality sampling tasks for ocean scalar field estimation. The multi-AUV-based cooperative localization and adaptive sampling system was proposed. Each AUV in the system broadcasted the collected sampling data to its teammates, and based on the data received, it corrected the location of itself based on the extended Kalman filter. With the collected sampling data, the AUV modeled the environmental scalar field with a Gaussian process and used a differential evolution path planner to plan its subsequent sampling path online. Simulation results showed that the proposed method effectively reduced the positioning error of AUVs, and improved the estimation accuracy of the environmental scalar field.