山地环境下无人机自主最优轨迹规划方法  

Autonomous Optimal Trajectory Planning Method for Unmanned Aerial Vehicles in Mountainous Environments

作　　者：张笑妍 钱程 梁小溪 程昊宇 ZHANG Xiaoyan;QIAN Cheng;LIANG Xiaoxi;CHENG Haoyu(Unmanned System Research Institute,Northwestern Polytechnical University,Xi’an 710072,China;Nation Key Laboratory of Unmanned Aerial Vehicle Technology,Northwestern Polytechnical University,Xi’an 710072,China;Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology,Northwestern Polytechnical University,Xi’an 710072,China;Shanghai Academy of Spaceflight Technology,Shanghai 201109,China)

机构地区：[1]西北工业大学无人系统技术研究院,西安710072 [2]西北工业大学无人飞行器技术全国重点实验室,西安710072 [3]西北工业大学无人机技术集成攻关大平台,西安710072 [4]上海航天技术研究院,上海201109

出　　处：《无人系统技术》2024年第5期24-32,共9页Unmanned Systems Technology

摘　　要：在无人机技术日益成熟的今天,复杂山地环境下的无人机轨迹规划成为了一个关键且极具挑战性的研究。针对复杂山地环境下无人机轨迹规划算法面临的实时性要求高、生成轨迹隐蔽性要求强等难题,提出了一种自适应麻雀搜索算法来应对复杂轨迹规划问题,得到最优轨迹。首先以最短路径为目标,考虑地形、机动等约束条件,设计贴合实际场景及工程应用需求的最优目标函数,引导算法求取最优解;随后改进传统麻雀算法的初始化过程,在算法初期解决了种群多样性低的问题,并提出自适应记忆种群迭代策略来保持种群多样性和收敛速度之间的平衡,在加快收敛速度的同时保证生成轨迹的全局最优性。最后,通过数值对比仿真及模拟山地环境轨迹规划仿真,验证了所提算法的优越性。验证表明,所提算法不仅提升了轨迹规划的实时性,还通过算法优化确保了轨迹的全局最优性和隐蔽性,为无人机轨迹规划提供了技术支持。With the increasing maturity of unmanned aerial vehicle(UAV)technology,UAV trajectory planning in complex mountainous environments has become a critical and challenging research.Considering the difficulties faced by UAV trajectory planning algorithms in complex mountainous environments,such as high real-time requirements and strong stealth requirements for generating trajectories,an adaptive sparrow search algorithm(ASSA)is proposed in this paper to cope with the complex trajectory planning problem and obtain the optimal trajectory.Firstly,the shortest path is taken as the goal,and the optimal objective function that fits the actual scenario and engineering application requirements is designed by considering the constraints such as terrain and maneuvering to guide the algorithm to find the optimal solution.Subsequently,the initialization process of the traditional sparrow search algorithm is improved to solve the problem of low population diversity at the early stage of the algorithm,and an adaptive memory population iteration strategy is proposed to maintain the balance between population diversity and convergence speed,which accelerates the convergence speed and ensures the global optimality of the generated trajectory at the same time.Finally,the superiority of ASSA is verified by numerical comparison simulation and simulation of trajectory planning in simulated mountain environment.The validation shows that the algorithm proposed in this paper not only improves the real-time trajectory planning,but also ensures the global optimality and covertness of the trajectory through algorithm optimization,providing technical support for UAV trajectory planning.

关 键 词：轨迹规划 自适应迭代 全局最优 麻雀搜索算法 无人机 山地环境 记忆种群 

分 类 号：V448.2[航空宇航科学与技术—飞行器设计]