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<p>This paper proposes an innovative multi-objective optimization method for the multiple Unmanned Aerial Vehicles (UAVs) rendezvous problem in 3D urban environments. By establishing a constrained multi-objective optimization model, the speeds and waypoints are treated as optimization variables to achieve temporal coordination of UAVs. Additionally, a composite path generation mechanism combining straight-line segments and circular-arc segments is introduced to ensure smooth flight trajectories. To address the optimization challenges under complex constraints, we develop the Cooperative Evolutionary Rendezvous Planning Algorithm (CERPA). This algorithm employs a collaborative mechanism to effectively harness valuable information from infeasible solutions, thereby significantly improving search efficiency. Moreover, a new problem-driven Local Search Module is integrated to further refine the quality of solutions. Experimental results demonstrate that the proposed planning method generates trajectories that strictly satisfy UAV kinematic constraints. Additionally, the proposed algorithm outperforms mainstream multi-objective optimization algorithms in both solution quality and convergence efficiency.</p>

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Multi-objective optimization for multi-UAV rendezvous planning in urban environments

  • 期刊论文

  • 发表日期：

    2026年4月13日

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  • Lijing Zhang,
  • Jiamin Yu,
  • Yulan Lu,
  • Xin Sun,
  • Xinhui Si,
  • Hu Zhang

摘要

This paper proposes an innovative multi-objective optimization method for the multiple Unmanned Aerial Vehicles (UAVs) rendezvous problem in 3D urban environments. By establishing a constrained multi-objective optimization model, the speeds and waypoints are treated as optimization variables to achieve temporal coordination of UAVs. Additionally, a composite path generation mechanism combining straight-line segments and circular-arc segments is introduced to ensure smooth flight trajectories. To address the optimization challenges under complex constraints, we develop the Cooperative Evolutionary Rendezvous Planning Algorithm (CERPA). This algorithm employs a collaborative mechanism to effectively harness valuable information from infeasible solutions, thereby significantly improving search efficiency. Moreover, a new problem-driven Local Search Module is integrated to further refine the quality of solutions. Experimental results demonstrate that the proposed planning method generates trajectories that strictly satisfy UAV kinematic constraints. Additionally, the proposed algorithm outperforms mainstream multi-objective optimization algorithms in both solution quality and convergence efficiency.