面向海洋环境自适应采样的多AUV协同定位

Multi-AUV cooperative localization in adaptive sampling for marine environmental monitoring

高效、准确的水质监测对海洋资源开发具有重要意义,自治式潜水器(AUV)在海洋环境监测中有广阔的应用前景。针对单个AUV执行面向海洋标量场估计的水质采样任务时存在的效率低、可靠性差、覆盖率不足和定位准确性差等问题,提出了一种基于多AUV的协同定位与自适应采样系统。系统中每个AUV定期向队友广播自己收集到的采样数据,并根据接收到的队友数据,基于扩展卡尔曼滤波器进行自定位矫正。根据收集到的采样数据,AUV以高斯过程建模环境标量场,使用差分进化路径规划器在线规划后续采样路径。仿真结果表明,所提方案有效降低了多AUV系统的定位误差,提升了对环境标量场的估计精度。

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.