基于改进遗传算法的多无人机搜索航路规划

Multi-UAV Search Route Planning Based on ImprovedGenetic Algorithm

针对多无人机对特定区域内的目标搜索问题,提出了一种基于贪婪算法改进变异操作的自适应遗传算法。依据先验情报对搜索区域进行栅格化处理,并结合无人机性能约束建立基于状态更新周期的协同搜索模型;引入类0-1编码将无人机航向控制序列与搜索概率进行关联;考虑到机载雷达对某一区域的重复探测会在一定程度上提高搜索概率,提出加入贪婪算子的贪婪变异策略,并引入策略选择阈值实现依据搜索概率变化对变异策略的动态调整,提高算法后期的局部搜索能力。仿真结果表明,改进的自适应遗传算法整体性能较好,具有较强的搜索能力和鲁棒性。

An adaptive genetic algorithm based on the greedy algorithm that improves mutation operation is proposed for the case of multi-UAV searching for targets in specific areas.The search area is rasterized according to the prior intelligence and the collaborative search model based on the state updating cycle is established considering UAV performance constraints.The 0-1 encoding is introduced to associate the UAV heading control sequence with the search probability.Considering that the repeated detection of a certain area by airborne radar could improve the search probability to a certain extent the greedy mutation strategy adding the greedy operator is proposed and the strategy selection threshold is introduced to realize the dynamic adjustment of the mutation strategy according to the variation of the search probability so as to improve the local search ability of the algorithm in the later stage of the process.The simulation results show that the improved adaptive genetic algorithm performs better and has strong search ability and robustness.