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<p>Frequent flood disasters caused by climate change may lead to tremendous economic and human losses along inland waterways. Emergency response and rescue vessels (ERRVs) play an essential role in minimizing losses and protecting lives and property. However, the path planning of ERRVs has mainly depended on expert experiences instead of rational decision making. This paper proposes an improved artificial potential field (APF) algorithm to optimize the shortest path for ERRVs in the rescue process. To verify the feasibility of the proposed model, eight tests were carried out in two water areas of the Yangtze River. The results showed that the improved APF algorithm was efficient with fewer iterations and that the response time of path planning was reduced to around eight seconds. The improved APF algorithm performed better in the ERRV’s goal achievement, compared with the traditional algorithm. The path planning method for ERRVs proposed in this paper has theoretical and practical value in flood relief. It can be applied in the emergency management of ERRVs to accelerate flood management efficiency and improve capacity to prevent, mitigate, and relieve flood disasters.</p>

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Path Planning for Emergency Response and Rescue Vessels in Inland Rivers by Improved Artificial Potential Field Algorithms

  • 期刊论文

  • 发表日期：

    2025年3月7日

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  • Jingyu Yu,
  • Qingyu Shi,
  • Wei Lin,
  • Jingfeng Wang,
  • Yuxue Pu

摘要

Frequent flood disasters caused by climate change may lead to tremendous economic and human losses along inland waterways. Emergency response and rescue vessels (ERRVs) play an essential role in minimizing losses and protecting lives and property. However, the path planning of ERRVs has mainly depended on expert experiences instead of rational decision making. This paper proposes an improved artificial potential field (APF) algorithm to optimize the shortest path for ERRVs in the rescue process. To verify the feasibility of the proposed model, eight tests were carried out in two water areas of the Yangtze River. The results showed that the improved APF algorithm was efficient with fewer iterations and that the response time of path planning was reduced to around eight seconds. The improved APF algorithm performed better in the ERRV’s goal achievement, compared with the traditional algorithm. The path planning method for ERRVs proposed in this paper has theoretical and practical value in flood relief. It can be applied in the emergency management of ERRVs to accelerate flood management efficiency and improve capacity to prevent, mitigate, and relieve flood disasters.