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<p>Efficient path planning and obstacle avoidance are key challenges for multi-UAV systems in complex 3D urban environments. This paper presents a multi-stage optimization framework that integrates task allocation, global path planning, and obstacle avoidance. A weighted clustering method is used for efficient patrol point allocation, ensuring balanced UAV workload. For global path planning, an enhanced Ant Colony Optimization (IACO) algorithm is proposed, incorporating dynamic pheromone updates and a 2-opt local search strategy to refine paths and improve optimization. The obstacle avoidance approach combines A* and Rapidly-exploring Random Trees (RRT) to navigate complex environments, with additional capabilities for handling dynamic obstacles when encountered, ensuring safe and efficient paths. Extensive simulations in large-scale urban environments validate the effectiveness of the proposed framework, demonstrating its ability to generate collision-free paths while minimizing energy consumption and flight time. The results show that the proposed framework is well-suited for multi-UAV operations in urban patrol tasks, offering significant improvements in operational efficiency and safety.</p>

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A multi-stage optimization strategy for multi-UAV path planning in complex urban 3D environments with obstacle avoidance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月18日

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  • Gang Liu,
  • Yu Tang,
  • Zhiping Tan,
  • Mingwei Fang

摘要

Efficient path planning and obstacle avoidance are key challenges for multi-UAV systems in complex 3D urban environments. This paper presents a multi-stage optimization framework that integrates task allocation, global path planning, and obstacle avoidance. A weighted clustering method is used for efficient patrol point allocation, ensuring balanced UAV workload. For global path planning, an enhanced Ant Colony Optimization (IACO) algorithm is proposed, incorporating dynamic pheromone updates and a 2-opt local search strategy to refine paths and improve optimization. The obstacle avoidance approach combines A* and Rapidly-exploring Random Trees (RRT) to navigate complex environments, with additional capabilities for handling dynamic obstacles when encountered, ensuring safe and efficient paths. Extensive simulations in large-scale urban environments validate the effectiveness of the proposed framework, demonstrating its ability to generate collision-free paths while minimizing energy consumption and flight time. The results show that the proposed framework is well-suited for multi-UAV operations in urban patrol tasks, offering significant improvements in operational efficiency and safety.