计算机应用研究2012，Vol.29Issue(12)：4469-4471,3.DOI:10.3969/j.issn.1001-3695.2012.12.016
基于正态云自适应遗传算法的无人机航路规划
Application of normal cloud based adaptive genetic algorithm in UAV path planning
摘要
Abstract
Sequential genetic algorithm (SGA) easily gets stuck at a local optimum and has a slow convergent speed. To overcome its shortage, this paper presented NCAGA for UAV path planning. It applied a novel method of encoding based on rectangular plane coordinate system, which simplified the complexity of encoding and achieved a higher planning speed. The improved genetic algorithm combined with the normal X-condition cloud generator to adjust the probability of crossover and mutation adaptively. The stable tendency of normal cloud contributed a higher convergence speed and character of randomness conduced to a lower possibility of premature. Simulation results demonstrate that NCAGA is able to plan path quickly that made UAV avoid the dangerous areas with a higher effectiveness and success rate, so that it has a wide application prospect.关键词
无人机/正态云模型/遗传算法/航路规划/自适应Key words
unmanned aerial vehiele( UAV) / normal cloud model/ genetic algorithm/ path planning/ adaptation分类
航空航天引用本文复制引用
成晓东,周德云,何鹏,张堃..基于正态云自适应遗传算法的无人机航路规划[J].计算机应用研究,2012,29(12):4469-4471,3.基金项目
航空科学基金资助项目(20115553021) （20115553021）
西北工业大学基础研究资助项目(JC20110222) （JC20110222）
