免疫算法在多无人机任务最优规划中的应用

Application of Immune Algorithm in Multi UAV Mission Optimal Planning

为提高无人机任务规划的规划效率,研究免疫算法在多无人机任务最优规划中的应用。先从俯仰角度、偏航角度和飞行高度等方面分析动力学约束条件,结合免疫算法结构建立任务最优规划问题的数学模型,将整体利益最大、路径和威胁代价最小为目标函数,设置约束准则;然后利用数字符串编码形成抗体,保证航程最优,同时计算抗体间亲和度,避免陷入局部最优;最后使用免疫记忆算子更新种群,采用高频变异方法改善规划空间搜索性能,设定合理的迭代次数作为算法终止条件,模型输出的结果即为任务最优规划方案。仿真实验表明,应用免疫算法的无人机即使在动态环境下也能在短时间内给出最优规划结果。

In order to improve the planning eficiency of uav mission planning,the application of immune algorithm in multiuav mission optimal planning was studied.Firstly,the dynamic constraints were analyzed from pitch Angle,yaw Angle and flight altitude.Combined with the structure of immune algorithm,the mathematical model of optimal mission planning problem was established.The maximum overall benefit and minimum path and threat cost were set as the objective function,and the constraint criteria were set.Then,the antibody was encoded by number string to ensure the optimal range,and the affinity between antibodies was calculated to avoid falling into the local optimum.Finally,the immune memory operator was used to update the population,and the high frequency mutation method was used to improve the performance of planning space search.A reasonable number of iterations was set as the termination condition of the algorithm,and the output result of the model was the optimal task planning scheme.The simulation results show that the uaw using immune algorithm can give optimal planning results in a short time even in dynamic environment.