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Synergistic path planning of multi-UAVs for air pollution detection of ships in ports

Author

Listed:
  • Shen, Lixin
  • Wang, Yaodong
  • Liu, Kunpeng
  • Yang, Zaili
  • Shi, Xiaowen
  • Yang, Xu
  • Jing, Ke

Abstract

The phenomena of the COVID-19 outbreak and the Arctic Iceberg melting over the past two years make us reconsider the impact our way of life has on the environment and the responsibility of business toward minimizing and potentially eliminating emissions. Increasing ship traffic in ports leads to the growing emission of air pollutants, which influences the air quality and public health in the surrounding areas. The International Maritime Organization (IMO) has adopted relevant regulations (e.g., Annex VI of IMO's pollution prevention treaty (MARPOL) and mandatory energy-efficiency measures) to address ship emissions. To ensure the effective implementation of such regulations and measures, air emission detection and monitoring has become crucial. In this paper, a dynamic multitarget path planning model is developed to realize multi-UAVs (Unmanned Aerial Vehicles) performing synergistic detection of ship emissions in ports. A path planning algorithm under a dynamic environment is developed to establish the model. This algorithm incorporates a Tabu table into particle swarm optimization (PSO) to improve its optimization ability, and it obtains the initial detection route of each UAV based on a “minimum ring” method. This paper describes a multi-UAVs synergistic algorithm to formulate the path reprogramming time in a dynamic environment by judging and cutting the “minimum ring”. This finding proves the improved efficiency of air pollution detection by UAVs. It provides useful insights for maritime and port authorities to detect ship emissions in practice and to ensure ship emission reduction for better air quality in the postpandemic era.

Suggested Citation

  • Shen, Lixin & Wang, Yaodong & Liu, Kunpeng & Yang, Zaili & Shi, Xiaowen & Yang, Xu & Jing, Ke, 2020. "Synergistic path planning of multi-UAVs for air pollution detection of ships in ports," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
  • Handle: RePEc:eee:transe:v:144:y:2020:i:c:s1366554520307766
    DOI: 10.1016/j.tre.2020.102128
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    References listed on IDEAS

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    2. Hu, Zhangchen & Chen, Heng & Lyons, Eric & Solak, Senay & Zink, Michael, 2024. "Towards sustainable UAV operations: Balancing economic optimization with environmental and social considerations in path planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    3. Song, Zhuzhu & Tang, Wansheng & Zhao, Ruiqing & Zhang, Guoqing, 2022. "Implications of government subsidies on shipping companies’ shore power usage strategies in port," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
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    6. He, Xinyu & He, Fang & Li, Lishuai & Zhang, Lei & Xiao, Gang, 2022. "A route network planning method for urban air delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).

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