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<p>The increasing number of maritime transport vessels has elevated ship distress accidents, emphasizing the imperative for effective Search and Rescue (SAR) operations. In this study, we introduce a coverage path planning framework for multiple Unmanned Aerial Vehicles (UAVs) to optimize maritime SAR missions by explicitly accounting for regional priorities. The proposed framework hierarchically incorporates these priorities and derives coverage paths using a heuristic algorithm based on Ant Colony Optimization (ACO) for each priority region. The algorithm consists of two key phases: the Region Allocation Phase (RAP), which utilizes a greedy approach guided by regional importance, and the Dynamic Refinement Phase (DRP), which employs ACO to ensure balanced resource usage. Comprehensive numerical experiments, utilizing actual sea coordinates, are conducted to validate the effectiveness of the proposed methodology. The proposed method outperforms both a MILP model and a reinforcement learning-based approach, particularly in large-scale instances. It demonstrates computational robustness and scalability, consistently generating high-quality solutions within practical computation times, even in complex environments. The methodology is further validated through experiments on irregular environments, confirming its adaptability and effectiveness.</p>

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Coverage path planning for multiple UAVs in prioritized regions using ant colony optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月21日

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  • Sung Won Cho,
  • Jae Hyeok Lee,
  • Youngrok Park,
  • Yanjie Zhou,
  • Wonhee Lee,
  • Chulung Lee

摘要

The increasing number of maritime transport vessels has elevated ship distress accidents, emphasizing the imperative for effective Search and Rescue (SAR) operations. In this study, we introduce a coverage path planning framework for multiple Unmanned Aerial Vehicles (UAVs) to optimize maritime SAR missions by explicitly accounting for regional priorities. The proposed framework hierarchically incorporates these priorities and derives coverage paths using a heuristic algorithm based on Ant Colony Optimization (ACO) for each priority region. The algorithm consists of two key phases: the Region Allocation Phase (RAP), which utilizes a greedy approach guided by regional importance, and the Dynamic Refinement Phase (DRP), which employs ACO to ensure balanced resource usage. Comprehensive numerical experiments, utilizing actual sea coordinates, are conducted to validate the effectiveness of the proposed methodology. The proposed method outperforms both a MILP model and a reinforcement learning-based approach, particularly in large-scale instances. It demonstrates computational robustness and scalability, consistently generating high-quality solutions within practical computation times, even in complex environments. The methodology is further validated through experiments on irregular environments, confirming its adaptability and effectiveness.