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Research on Path Optimization for Collaborative UAVs and Mothership Monitoring of Air Pollution from Port Vessels

Author

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  • Lixin Shen

    (School of Shipping Economics and Management, Dalian Maritime University, Dalian 116026, China)

  • Jie Sun

    (School of Shipping Economics and Management, Dalian Maritime University, Dalian 116026, China)

  • Dong Yang

    (School of Shipping Economics and Management, Dalian Maritime University, Dalian 116026, China)

Abstract

The seriousness of vessel air pollution has forced the International Maritime Organization (IMO) to introduce a series of relevant laws and regulations. This paper proposes a monitoring scheme based on the synergistic operation of motherships and UAVs. This scheme innovatively adopts a harbor sea patrol vessel or the other official vessel (mothership) as the mobile power supply base for UAVs and realizes efficient and accurate monitoring of vessel air pollution in the pre-monitored area at sea by carrying multiple UAVs. The focus of this paper is on the path optimization problem for multi-UAV collaboration with mothership (MUCWM) monitoring, where the objective is to minimize the total monitoring time for MUCWM. The following three main aspects are studied in this paper: (1) multi-UAV monitoring path optimization; (2) the collaboration mechanism between the mothership and multiple UAVs; and (3) mothership traveling path optimization. In order to effectively solve the above problems, this thesis constructs a path optimization model for multi-UAV collaborative mothership monitoring of air pollution from vessels in port waters; solves the model using the improved adaptive differential evolution (IADE) algorithm; and verifies the effectiveness of the model and the algorithm by using the position data in the Automatic Identification System (AIS) of vessels in Ningbo Zhoushan Port. Through the performance comparison and sensitivity analysis of the algorithm, it is confirmed that the algorithm can effectively solve the path planning problem of the collaborative operation between the mothership and multiple UAVs. The research results in this paper not only help to reduce the air pollution level of harbor vessels and improve the efficiency of sea cruising but also play an important supporting role in the enforcement of relevant emission regulations.

Suggested Citation

  • Lixin Shen & Jie Sun & Dong Yang, 2024. "Research on Path Optimization for Collaborative UAVs and Mothership Monitoring of Air Pollution from Port Vessels," Sustainability, MDPI, vol. 16(12), pages 1-33, June.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:12:p:4948-:d:1411785
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    References listed on IDEAS

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    1. Lingyue Li & Suixiang Gao & Wenguo Yang, 2022. "The enforcement of ECA regulations: inspection strategy for on-board fuel sampling," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2551-2576, November.
    2. Zajac, Sandra & Huber, Sandra, 2021. "Objectives and methods in multi-objective routing problems: a survey and classification scheme," European Journal of Operational Research, Elsevier, vol. 290(1), pages 1-25.
    3. Wang, Zheng & Sheu, Jiuh-Biing, 2019. "Vehicle routing problem with drones," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 350-364.
    4. Xia, Jun & Wang, Kai & Wang, Shuaian, 2019. "Drone scheduling to monitor vessels in emission control areas," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 174-196.
    5. Iman Dayarian & Martin Savelsbergh & John-Paul Clarke, 2020. "Same-Day Delivery with Drone Resupply," Transportation Science, INFORMS, vol. 54(1), pages 229-249, January.
    6. Niels Agatz & Paul Bouman & Marie Schmidt, 2018. "Optimization Approaches for the Traveling Salesman Problem with Drone," Transportation Science, INFORMS, vol. 52(4), pages 965-981, August.
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