基于Q学习算术优化算法的无人机三维航迹规划

Three-Dimensional UAV Path Planning Based on Q-Learning Arithmetic Optimization Algorithm

针对传统方法求解无人机三维航迹规划易导致规划代价高、精度差和容易陷入局部最优的不足,提出基于Q学习算术优化算法的无人机三维航迹规划算法。为了提升算术优化算法的寻优精度,引入Circle混沌映射提高初始种群多样性和分布均匀性,引入Q学习根据个体状态自适应调整数学优化加速函数更新,均衡算法全局搜索与局部开发,设计最优解邻域扰动优化全局搜索能力。通过建立无人机三维航迹规划模型,将航迹规划转化为多目标函数优化问题,并利用改进算法求解无人机三维航迹规划,以综合考虑航迹代价、地形代价和边界代价的目标函数评估粒子适应度,对航迹规划迭代寻优。仿真实验结果表明,所提算法规划的航迹具有更低的总代价和适应不同复杂地形环境的稳定性。

To overcome the shortomings of traditional methods in solving three-dimensional UAV path planning,such as high planning Costs,poor accuracy and prone to obtain a local optimum,a three-dimensional UAV path planning algorithm based on Q-learming arithmetie optimization algorithm is proposed.In order to improve the optimization accuracy of the arihmetic optimization algorithm,the Circle chaotic mapping is introduced to improve the diversity and distribution uniformity of the initial population.Q-learning is introduced to adaptively adjust the updating of the acceleration function.The global searching and local development of the algorithm are made balanced.The perturbations in the optimal solution's neighbothood are designed to optimize the global searching capability.By establishing a three-dimensional UAV path planning model,the path planning is transformed into a muli-objetive function optimization problem,and the improved algorithm is used to solve the three-dimensional UAV path planning problem.The trajectory objctive function that comprehensively considers the trajectory cost,the terrain cost and the boundary cost is used to evaluate the fitness of the particles,and the path planning is optimized through iterations.The simulation results show that the trajectory obtained by the proposed algorithm has lower total costs and the stability to adapt to different complex termain environments.