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Multi-UAV Three-Dimensional Path Planning Based on Improved ...

Multi-UAV Three-Dimensional Path Planning Based on Improved Grey Wolf Optimization Algorithm

作     者:Mingyu Zhang Feng Liu Yujie Wang Yaqing Yan ZhengXian Wei 

作者单位:Institute of Advanced Control Systems Beijing Jiaotong University Systems Engineering Research Institute China State Shipbuilding Corporation 

会议名称:《第43届中国控制会议》

会议日期:1000年

学科分类:12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 082503[工学-航空宇航制造工程] 0835[工学-软件工程] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术(可授工学、理学学位)] 

关 键 词:Grey Wolf Optimization Algorithm Multi-UAV 3D Trajectory Planning Convergence Factor Reward-Penalty Mechanism 

摘      要:To address the challenges of multi-unmanned aerial vehicle(UAV) trajectory planning in three-dimensional complex environments, this study proposes a method based on the Improved Grey Wolf Optimization Algorithm for Multi-UAV 3D Trajectory Planning. The approach simulates real geographical environments, establishing three-dimensional terrain and no-fly zone models. Building upon the foundation of single UAV trajectory planning, the proposed method incorporates collaborative constraints for multi-UAV coordination, forming an evaluation function for multi-UAV collaborative trajectory planning. In order to solve the limitations of the standard Grey Wolf Algorithm, which is prone to local optima and exhibits suboptimal convergence rates, an improved convergence factor strategy and a reward-penalty mechanism in the optimization process are introduced. Comparative evaluations against several relevant algorithms validate the superior feasibility of the proposed approach. Simulation results demonstrate that, compared to other algorithms, the proposed method achieves smaller trajectory costs, faster convergence rates, and more stable performance.

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