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

Jiaxin ZHANG; Senlin ZHANG; Meiqin LIU; Shanling DONG; Ronghao ZHENG

doi:10.11959/j.issn.2096-6652.202250

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

Special Topic: Autonomous Underwater Vehicle | 更新时间：2024-06-05

- Multi-AUV cooperative localization in adaptive sampling for marine environmental monitoring
- Chinese Journal of Intelligent Science and Technology Vol. 4, Issue 4, Pages: 503-512(2022)
- 作者机构：
  
  1. 浙江大学电气工程学院，浙江杭州 310027
  2. 浙江大学工业控制技术国家重点实验室，浙江杭州 310027
  3. 西安交通大学人工智能与机器人研究所，陕西西安 710049
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202250
  CLC： TP242.6
- Published Online：2022-12，
  
  Published：15 December 2022
- 稿件说明：
移动端阅览
Jiaxin ZHANG, Senlin ZHANG, Meiqin LIU, et al. Multi-AUV cooperative localization in adaptive sampling for marine environmental monitoring[J]. Chinese journal of intelligent science and technology, 2022, 4(4): 503-512.
DOI：

Jiaxin ZHANG, Senlin ZHANG, Meiqin LIU, et al. Multi-AUV cooperative localization in adaptive sampling for marine environmental monitoring[J]. Chinese journal of intelligent science and technology, 2022, 4(4): 503-512. DOI： 10.11959/j.issn.2096-6652.202250.

摘要

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。

Abstract

Efficient and accurate water quality monitoring is of great significance to the development of marine resources

and Special Topic: 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.

关键词

Keywords

references

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