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兵工学报 ›› 2012, Vol. 33 ›› Issue (3): 295-300.doi: 10.3969/j.issn.1000-1093.2012.03.008

• 论文 • 上一篇    下一篇

无人飞行器海上航迹规划差分进化算法研究

傅阳光1,2, 周成平2, 胡汉平2   

  1. (1.南华大学 计算机科学与技术学院, 湖南 衡阳 421001;2.华中科技大学 图像识别与人工智能研究所湖北 武汉 430074)
  • 收稿日期:2010-12-20 修回日期:2010-12-20 上线日期:2014-03-04
  • 作者简介:傅阳光(1984—),男,博士研究生

Research on Differential Evolution Algorithm for Path Planning for Unmanned Aerial Vehicle inOcean Environment

FU Yang-guang1,2, ZHOU Cheng-ping2, HU Han-ping2   

  1. (1.School of Computer Science and Technology, University of South China, Hengyang 421001,2.Institute for Pattern Recognition and Artificial Intelligence,Huazhong University of Science and Technology,Wuhan 430074, Hubei, China)
  • Received:2010-12-20 Revised:2010-12-20 Online:2014-03-04

摘要: 为研究海洋环境下的无人飞行器(UAV)航迹规划问题,提出了一种基于差分进化算法(DE)的航迹规划方法。该方法通过对规划环境进行预处理将岛屿处理成地形威胁区,使问题简化为二维平面规划。采用实数编码方式对航迹进行编码,建立了航迹代价函数的数学模型,从航迹质量、算法稳定性和收敛速度3个方面比较了DE与遗传算法(GA)和粒子群优化算法(PSO)的性能。仿真实验结果表明,所提方法能在复杂的海洋环境下为飞行器规划出一条安全的可飞航迹。

关键词: 飞行器控制、导航技术, 无人飞行器, 航迹规划, 地形预处理, 差分进化, 遗传算法, 粒子群优化

Abstract: To investigate the path of unmanned aerial vehicle (UA) in ocean environment, a method based on the differential evolution(DE)is proposed. It pretreats the planning environment and takes all islands as threatened areas, the path planning problem is simplified as a two-dimensional planning problem. A real number coding is used to represent the candidate paths, and a mathematical model of path cost is established. The performance of differential evolution algorithm is compared with that of genetic algorithm(GA)and particle swarm optimization(PSO)in terms of path quality, robustness and convergence speed. The experimental results demonstrate that the proposed method is able to generate a safe and flyable path for UAV in a complex ocean environment.

Key words: control and navigation technology of aerocraft, unmanned aerial vehicle, path planning, terrain pretreatment, differential evolution, genetic algorithm, particle swarm optimization

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