Chinese Journal of Intelligent Science and Technology ›› 2022, Vol. 4 ›› Issue (4): 503-512.doi: 10.11959/j.issn.2096-6652.202250

• Special Topic: Autonomous Underwater Vehicle • Previous Articles     Next Articles

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

Jiaxin ZHANG1, Senlin ZHANG1,2, Meiqin LIU1,2,3, Shanling DONG1,2, Ronghao ZHENG1,2   

  1. 1 College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
    2 State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
    3 Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China
  • Revised:2022-11-04 Online:2022-12-15 Published:2022-12-01
  • Supported by:
    The NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization(U1809212);The NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization(U1909206);The Fundamental Research Funds for the Zhejiang Provincial Universities(2021XZZX014)


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.

Key words: Special Topic: Autonomous Underwater Vehicle, cooperative localization, Gaussian process, adaptive sampling, path planning

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