基于自适应遗传算法的无人机航迹规划方法研究(英文)

UAV Path Planning Based on Adaptive Genetic Algorithm

随着攻防系统的发展与完善,实现飞行器有效突防越来越困难,而采用航迹规划技术能够有效的提高飞行器的突防概率。基于此,首先研究了参考航迹的角度、高度以及航迹段长度等约束条件;其次对航迹编码方式进行了改进,采用全实数的双向链表的编码方式;对自适应遗传算法的交叉和变异概率的计算方法、交叉算子和变异算子进行了改进,并应用该算法在求解航迹规划问题上进行了仿真研究,对采用不同的变异算子所得结果进行了对比分析。仿真计算的结果表明,该算法能够规划出一条满足要求的参考航迹,采用组合变异算子能取得比采用单个变异算子更优的参考航迹。

With the development of the warfare, it becomes more and more difficult for the military aircraft to attack the target. Path planning is one of the available methods to increase the survival probability. In recent years, genetic algorithm （GA） has been successfully applied to path planning problems for unmanned aerial vehicle （UAV） systems, including single- and multi-vehicle systems. A new encoding method was designed by using bilinear-chain node and the mutation operator with combined operator with reconstruction operator and disturbance operator was improved. An adaptive genetic algorithm （ADGA）, which determined the optimal path between the nodes with respect to a set of cost factors and constraints, was applied to the optimal path planning. Example simulation shows that the new algorithm satisfies the requirements in the computation efficiency and the precision of the solution. The algorithm is easy to be realized. Its practicability is improved.