基于改进GWO的多无人机三维协同航迹规划

Multiple Unmanned Aerial Vehicles Three-dimensional Cooperative Route Planning Based on Improved GWO Algorithm

针对GWO算法求解多无人机协同航迹时存在协同性差、易陷入局部最优、收敛速度慢以及求解精度差的问题,提出一种基于改进GWO的多无人机三维协同航迹规划方法。首先,建立多无人机三维协同航迹规划数学模型,以消耗代价、高度代价、威胁代价、空间约束、时间约束5者的加权和以及惩罚项作为目标函数;其次,利用贪心算法和Tent映射相结合的方法来提高种群适应度,并保留种群的多样性来减小陷入局部最优的可能性;再改进收敛因子来提高算法收敛速度;然后,设计一种动态权重位置更新方法来提高算法的探索和开发能力;最后,将改进的GWO算法应用于多无人机航迹规划问题求解。与GWO算法和CSGWO算法对比的仿真结果表明,所提出的改进GWO算法在求解精度上分别提高了64.8%和16.7%,在收敛速度上分别提升了28.5%和25.4%,并且协同能力明显优于对比算法。

To overcome the problems of poor cooperation,immersing local minimization,low convergence speed and poor solving accuracy in solving the collaborative route by GWO algorithm for multiple unmanned aerial vehicles,an improved GWO-based three-dimensional collaborative route planning algorithm for multiple unmanned aerial vehicles is proposed.Firstly,a three-dimensional collaborative trajectory planning mathematical model for multiple unmanned aerial vehicles is established,using the weighted sum of consumption cost,height cost,threat cost,spatial constraint,time constraint,and penalty term as the objective function.Secondly,the Greedy algorithm and Tent mapping are combined to improve the fitness of the population and preserve the diversity of the population to reduce the possibility of falling into local optima;then we optimize the convergence factor to improve the rate of convergence of the algorithm.Afterwards,we design a dynamic weight position update method to enhance the exploration and development capabilities of the algorithm.Finally,the improved GWO algorithm is applied to solve the trajectory planning problem of multiple unmanned aerial vehicles,and compared with GWO algorithm and CSGWO algorithm.The simulation results indicate that the proposed improved GWO algorithm enhance the solution accuracy by 64.8%and 16.7%,as well as the convergence speed by 28.5%and 25.4%,respectively.Additionally the synergy ability is significantly better than that of the comparison algorithms.