引用本文:吴文海,郭晓峰,周思羽.基于改进约束差分进化算法的动态航迹规划[J].控制与决策,2020,35(10):2381-2390
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基于改进约束差分进化算法的动态航迹规划
吴文海,郭晓峰,周思羽
(海军航空大学青岛校区航空仪电控制工程与指挥系,山东青岛266041)
摘要:
为解决三维复杂环境下无人机动态航迹规划问题,提出一种基于改进约束差分进化算法的动态航迹规划方法,以满足对实时性及动态搜索精度的要求.首先,根据无人机航迹规划特点将其描述为包括飞行约束及威胁约束在内的约束优化问题,并构造目标代价函数和约束限制函数;其次,将广义反向学习和自适应排序变异操作引入到约束差分进化算法中,以提高算法的多样性、收敛速度和寻优精度;最后,利用自适应权衡模型对各状态下的约束限制进行处理,充分利用“精英”个体信息,实现对目标适应值的合理转换.通过仿真实验以及与3种先进约束差分进化算法比较表明:所提方法能够有效实现静态及动态威胁回避,规划出安全适航的飞行路径,实现地形跟随;相较于其他3种算法,所提方法具有寻优性能好、鲁棒性强、收敛速度快和可靠性高等优势.
关键词:  动态航迹规划  地形跟随  威胁回避  约束差分进化算法
DOI:10.13195/j.kzyjc.2018.1732
分类号:TP301.6
基金项目:国家重点研发计划项目(2018YFC0806900,2016YFC0800606,2016YFC0800310).
Dynamic route planning based on improved constrained differential evolution algorithm
WU Wen-hai,GUO Xiao-feng,ZHOU Si-yu
(Department of Aviation Control and Command,Qingdao Branch of Naval Aeronautics University,Qingdao266041,China)
Abstract:
Aiming at the problem of three-dimensional dynamic route planning for unmanned aerial vehicles(UAV), an improved constrained differential evolution(CDE) algorithm is proposed so as to meet the requirements of instantaneity and dynamic search accuracy. Firstly, this paper formulates the route planning problem for the UAV as a constrained optimization problem and constructs the objective functions and constraint functions according to the constraints of flight and threats. Then, the diversity, convergence and accuracy of the algorithm are improved through introducing generalized opposition-based learning and adaptive ranking mutation operators into the CDE algorithm. Finally, the adaptive trade-off model is applied to handle the constraints in each state, and the information of elite individual is fully utilized to achieve a reasonable conversion of the fitness. Experiment results and comparisons with 3 state-of-the-art CDEs show that the proposed method is able to plan a safe flight route which can implement static and dynamic threat avoidance effectively and realize terrain following. Compared with other three algorithms, the presented method has the advantages of terrific optimization performance, strong robustness, good convergence and high reliability.
Key words:  dynamic route planning  terrain following  threat avoidance  constrained differential evolution

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