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针对传统无人机路径规划算法存在规划效率低以及无法满足特定任务需求的缺点,提出了基于改进蚁群优化算法的无人机路径规划算法。首先,将待规划区域栅格化,给每一个网格按顺序编号;其次,在路径搜索时引入了一种双向搜索机制,对信息素的更新规则和下一步节点的选择方法做出改进;最后,提出了一种新的方法来整合两组蚂蚁生成的路径,并给出了若干仿真试验结果。结果表明,所提算法相比传统算法更能有效避免过早陷入局部最优,收敛速度加快,生成满足任务约束的最短路径。
Aiming at the shortcomings of traditional UAV path planning algorithms such as low planning efficiency and inability to meet the requirements of specific tasks, a UASP algorithm based on improved ant colony optimization algorithm is proposed. Firstly, the area to be planted is rasterized, and each grid is numbered in sequence. Secondly, a two-way search mechanism is introduced in path searching to improve the rules of pheromone updating and the selection method of nodes in the next step. Finally, a new method is proposed to integrate the ants generated by two groups, and some simulation results are given. The results show that the proposed algorithm can effectively avoid premature convergence into local optimum and accelerate convergence and generate the shortest path satisfying the task constraints.