Application of particle swarm algorithm in route planning of UAV

被引：6
|
作者：
Ni T.-Q. [1 ,3 ]
Wang J.-D. [1 ]
Liu Y.-A. [2 ]
机构：
[1] College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics
[2] School of Information Engineering, Jiangnan University
[3] No.723 Institute of China Shipbuilding Industry Corporation
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2011年 / 33卷 / 04期
关键词：
Particle swarm; Route planning; Threat avoidance; Unmanned aerial vehicle (UAV);
D O I：
10.3969/j.issn.1001-506X.2011.04.19
中图分类号：
学科分类号：
摘要：
The UAV faces a flinty challenge when executing aerial reconnaissance, surveillance and campaign as the environment of modern battle field is becoming more and more complicated and large. A route planning algorithm is proposed to increase efficiency of campaign and survival of unmanned aerial vehicles (UAV). The algorithm first search a coarse route as fast as possible based on threat avoidance technique according to threat areas in battle field. Then it optimizes the coarse route globally by employing the particle swarm algorithm and the idea of intercross and variation in genetic algorithm. The result of simulating shows the algorithm can find an optimized safe route by consuming less iteration times.
引用
收藏
页码：806 / 810
页数：4
相关论文
共 17 条
  • [1] Qu Y.H., Study on the key techniques of trajectory planning and integrated navigation of UAV, (2006)
  • [2] Robert J.S., Robust algorithm for real-Time route planning, IEEE Trans. on Aerospace and Electronic Systems, 36, 3, pp. 869-877, (2000)
  • [3] Bortoff S.A., Path planning for UAVS, Proc. of the American Control Conference, pp. 364-368, (2000)
  • [4] Xiao Q.K., Gao X.G., The study of path planning based on improved spatial Voronoi graph, Nature Science Improvement, 16, 2, pp. 232-237, (2006)
  • [5] Wang Z.W., Guo G., Present situation and future development of mobile robot navigation technology, Robot, 25, 5, pp. 470-474, (2003)
  • [6] Ma X.L., Ye W., Fan H.D., Research of computer simulation on route planning of low altitude penetration, Journal of System Simulation, 16, 3, pp. 458-464, (2004)
  • [7] Hsiao Y.T., Chuang C.L., Chien C.C., Ant colony optimization for best path planning, Proc. of the International Symposium on Communications and Information Technology, pp. 109-113, (2004)
  • [8] Lee C.G., Cho D.H., Jung H.K., Niche genetic algorithm with restricted competition selection for multimodal function optimization, IEEE Trans. on Magnetics, 35, 1, pp. 1722-1725, (1999)
  • [9] Ai-Hasan S., Vachtsevanos G., Intelligent route planning for fast autonomous vehicles operating in a large natural terrain, Robotics and Autonomous Systems, 40, 1, pp. 1-24, (2002)
  • [10] Jose B.C., Genshe C., Li D.X., Et al., Particle swarm optimization for resource allocation in UAV cooperative control, AIAA Guidance, Navigation, and Control Conference, pp. 1-11, (2004)
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