无线电工程

山区地势具有陡峭、沟深壑大的环境特点，导致基于启发式算法的山区无人机路径规划速度慢、质量差，针对该问题提出了基于自适应动作策略蜣螂算法的路径规划方法。以路径长度、飞行安全性以及路径平滑度构建路径规划目标函数；在蜣螂算法中引入种群相似性动作变异策略和反向学习策略，平衡局部优化和全局优化能力；通过对比麻雀算法、蜣螂算法和灰狼算法在12个基准函数上的算法性能，结果表明所提方法具有更快的收敛速度、不易陷入局部最优。山区路径规划仿真实验表明，所提方法比蜣螂算法的路径规划质量提高了37.66%。

Due to the environmental characteristics of steep terrain and large gullies in mountainous areas, the UAV path planning in mountainous areas based on heuristic algorithm has slow speed and poor quality. To solve this problem, a path planning method of dung beetle algorithm based on adaptive action strategy is proposed. Firstly, the objective function of path planning is constructed with path length, flight safety and path smoothness. Then, the population similarity action mutation strategy and opposition-based learning strategy are introduced into the dung beetle algorithm to balance the local optimization and global optimization ability. Finally, by comparing the performance of sparrow algorithm, dung beetle algorithm and grey wolf algorithm on 12 benchmark functions, the results show that the proposed method has faster convergence speed and is not easy to fall into local optimum. The simulation experiment of path planning in mountainous area shows that the path planning quality of the proposed method is 37.66% higher than that of the dung beetle algorithm.