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<p>This paper presents a novel phased trajectory planning method for unmanned aerial vehicle (UAV) swarm cooperation in complex dynamic environments. The proposed approach consists of two main phases: global planning and online local replanning. In the global planning phase, an improved genetic algorithm is developed with a multi-objective optimization model to optimize trajectory length and smoothness. This algorithm incorporates a redundant waypoint deletion operator and a directional selection method. The local replanning phase employs a hierarchical strategy that achieves high efficiency by decoupling the three-dimensional search into two-dimensional pathfinding and altitude planning, incorporating an improved variable-step sparse A* algorithm to reduce three-dimensional search complexity. To address trajectory discretization issues, a quadratic smoothing optimization method based on Z-shaped trajectory segment identification is introduced. Simulation results demonstrate that the proposed method achieves a 99&#xa0;km average range in global planning under a 60 × 60 × 5&#xa0;km combat scenario, reduces replanning response time by 52.5% compared to traditional three-dimensional A* algorithm, and improves trajectory smoothness by 41.2%. The method effectively combines global optimization capability with real-time response performance for multi-unmanned aerial vehicle (multi-UAV) cooperative missions in complex adversarial scenarios.</p>

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Phased Trajectory Planning for UAV Swarm Cooperation in Complex Dynamic Environments

  • 期刊论文

  • 发表日期：

    2026年4月18日

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  • Wei Zhou,
  • Canting Dai,
  • Qiyuan Yin,
  • Bohan Li

摘要

This paper presents a novel phased trajectory planning method for unmanned aerial vehicle (UAV) swarm cooperation in complex dynamic environments. The proposed approach consists of two main phases: global planning and online local replanning. In the global planning phase, an improved genetic algorithm is developed with a multi-objective optimization model to optimize trajectory length and smoothness. This algorithm incorporates a redundant waypoint deletion operator and a directional selection method. The local replanning phase employs a hierarchical strategy that achieves high efficiency by decoupling the three-dimensional search into two-dimensional pathfinding and altitude planning, incorporating an improved variable-step sparse A* algorithm to reduce three-dimensional search complexity. To address trajectory discretization issues, a quadratic smoothing optimization method based on Z-shaped trajectory segment identification is introduced. Simulation results demonstrate that the proposed method achieves a 99 km average range in global planning under a 60 × 60 × 5 km combat scenario, reduces replanning response time by 52.5% compared to traditional three-dimensional A* algorithm, and improves trajectory smoothness by 41.2%. The method effectively combines global optimization capability with real-time response performance for multi-unmanned aerial vehicle (multi-UAV) cooperative missions in complex adversarial scenarios.