随着科学技术和经济的发展,无人机的应用越来越广泛。然而,现有的无人机航迹规划方法存在成本高、智能化程度低的局限性。鉴于此,采用灰狼算法实现无人机群的协同轨迹优化。然而,研究发现,灰狼优化算法(GWO)存在协作弱的问题。在这项研究中,在传统的GWO信息素因子的基础上引入对其进行改进。.针对群智能优化算法在动态威胁下性能不稳定的问题,采用深度强化学习优化模型。构建了基于改进灰狼算法的无人机群轨迹规划模型。通过实验分析,改进灰狼算法的最优适应度值低于灰狼算法的0.43。与其他算法相比,该算法的适应度值显著降低,稳定性较高。在复杂的场景中,改进的灰狼算法轨迹长度为70.51km,规划时间为5.92s,这显然优于其他算法。研究设计模型规划的路径长度为58.476km,明显小于其他三个模型。规划时间为5.33s,路径扩展点数量为46。本研究设计的无人机群轨迹规划模型的指标值均小于其他三种模型。通过分析结果,该模型可以实现低成本的轨迹优化,为无人机任务执行提供更合理的技术支持。
With the development of science and technology and economy, UAV is used more and more widely. However, the existing UAV trajectory planning methods have the limitations of high cost and low intelligence. In view of this, grey Wolf algorithm is being used to achieve collaborative trajectory optimization of UAV groups. However, it is found that the Grey Wolf optimization algorithm (GWO) has the problem of weak cooperation. In this study, based on the traditional GWO pheromone factor is introduced to improve it.. Aiming at the problem of unstable performance of swarm intelligence optimization algorithm under dynamic threat, deep reinforcement learning is used to optimize the model. An unmanned aerial vehicle swarm trajectory planning model was constructed based on the improved grey wolf algorithm. Through experimental analysis, the optimal fitness value of the improved grey wolf algorithm was lower than 0.43 of the grey wolf algorithm. Compared with other algorithms, the fitness value of this algorithm is significantly reduced and the stability is higher. In complex scenarios, the improved grey wolf algorithm had a trajectory length of 70.51 km and a planning time of 5.92 s, which was clearly superior to other algorithms. The path length planned by the research and design model was 58.476 km, which was significantly smaller than the other three models. The planning time was 5.33 s and the number of path extension points was 46. The indicator values of the Unmanned Aerial Vehicle swarm trajectory planning model designed by this research were all smaller than the other three models. By analyzing the results, the model can achieve low-cost trajectory optimization, providing more reasonable technical support for unmanned aerial vehicle mission execution.