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AUV Path Planning Based on Differential Evolution with Environment Prediction

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Abstract

Energy-efficient path planning is essential for the autonomous underwater vehicle (AUV)-based ocean exploration. Existing static environment-based AUV path planners do not work well in dynamic ocean environments. A novel onboard sensing system-based AUV path planning strategy is proposed, and it is suitable for a regional dynamic environment to improve the energy utilization efficiency of an AUV working in a small-scale and dynamic mission area. Firstly, unlike the existing methods, the onboard sensing system including horizontal acoustic doppler current profile and detecting sonar is used to obtain environmental information, and the probabilistic multiple hypothesis tracker and Kalman filter are employed to carry out multi-step prediction of the environment. After that, the differential evolution algorithm is introduced as the optimizer, and a novel prediction-based path evaluator is designed to evaluate the fitness of possible paths. Besides, a novel prediction-based online re-planning strategy is designed, which is beneficial to reduce the impact of forecast error and the planning is thus closed-loop. Finally, multiple simulation experiments are designed to verify the effectiveness and superiority of the path planner, and the results show that the proposed planning strategy can reduce the AUV energy consumption by at least 4.6% compared with static environment-based planners.

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Code Availability

The MATLAB codes used and analyzed during the study are available from the first author on reasonable request.

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Funding

This work was supported by the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization under Grants U1709203, U1809212 and U1909206, the Zhejiang Provincial Natural Science Foundation of China under grant LZ19F030002, the Key Research and Development Program of Zhejiang Province under Grant 2019C03109, and the NSFC under Grant 62088102.

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Authors and Affiliations

  1. The State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027, China

    Jiaxin Zhang & Meiqin Liu

  2. College of Electrical Engineering, Zhejiang University, Hangzhou, 310027, China

    Jiaxin Zhang, Senlin Zhang & Ronghao Zheng

  3. The Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, 710049, China

    Meiqin Liu

Authors
  1. Jiaxin Zhang
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  2. Meiqin Liu
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  3. Senlin Zhang
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  4. Ronghao Zheng
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Contributions

J. Zhang and M. Liu contributed to the design and implementation of the research, the analysis of the results and the writing of the first draft of the manuscript. S. Zhang and R. Zheng commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Meiqin Liu.

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Zhang, J., Liu, M., Zhang, S. et al. AUV Path Planning Based on Differential Evolution with Environment Prediction. J Intell Robot Syst 104, 23 (2022). https://doi.org/10.1007/s10846-021-01533-9

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