基于改进金豺算法的无人机三维航迹规划

UAV 3D path planning based on improved golden jackal algorithm

针对传统金豺优化算法(GJO)在求解无人机航迹规划时存在的稳定性差、寻优能力不足等问题,提出一种改进金豺优化算法(IGJO)。首先使用改进Tent混沌映射初始化种群;然后结合北方苍鹰优化算法思想,开发一种新型探索阶段位置更新方式;其次,采用动态能量因子策略来转换能量递减方式;最后,提出一种用于平衡探索和开发的阈值双重自适应t分布扰动变异策略,完成对GJO算法的改进。另外,建立一个无人机三维航迹规划模型,并利用IGJO算法求解以航程代价、高度代价等为目标函数的无人机三维航迹规划问题。结果表明,IGJO算法可适用于不同的复杂地形环境,且规划的航迹具有最优的成本代价。

In allusion to the poor stability and inadequate optimization capability of the traditional golden jackal optimization(GJO)algorithm in solving unmanned aerial vehicle(UAV)trajectory planning,an improved golden jackal optimization(IGJO)algorithm is proposed.An improved Tent chaotic mapping is used to initialize the population.Then,integrating the concept of the northern goshawk optimization algorithm,a new exploration phase position updating method is developed.A dynamic energy factor strategy is employed to transform the energy decay mode.A dual-threshold adaptive t-distribution perturbation strategy for balancing exploration and development is proposed to improve the GJO algorithm.A UAV 3D(three-dimensional)path planning model is established,and the IGJO algorithm is utilized to improve UAV 3D path planning with objective functions such as flight distance cost and altitude cost.The results show that the IGJO algorithm can be used to different complex terrain environments,and the planned flight path has the optimal cost.