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
Key words
Trajectory Planning/Adaptive Iteration/Global Optimization/Sparrow Search Algo-rithm/Unmanned Aerial Vehicle/Mountainous Environments/Memory Population
维普
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