基于改进启发式蚁群算法的无人机自主航迹规划

无人机自主航迹规划是未来无人机作战使用的关键技术难题。针对传统航迹规划方法存在的求解效率不高、实时性较差、容易陷入局部最优等缺点，提出一种基于改进启发式蚁群算法的无人机航迹规划。算法前期使用Dijkstra 算法进行初始化航迹，引入启发式信息，提高搜索效率；采用Logistic 混沌映射初始化信息素，增加解的多样性，提高算法收敛速度；算法中、后期采用多航迹选择策略和模拟退火机制，提高全局搜索能力，避免因收敛速度过快，陷入局部最优解。对该算法进行仿真分析，结果表明：在存在威胁和障碍的复杂环境中，本文的改进蚁群算法与标准蚁群算法相比，能够有效规划出一条从起点到终点的航迹，并且寻优精度更高，收敛速度更快，具有一定应用价值。

Autonomous path planning for unmanned aerial vehicle based on improved heuristic ant colony algorithm

Autonomous path planning of UAV is a key technical problem for future UAV operation. In view of the shortcomings of traditional route planning methods, such as low efficiency, poor real-time performance, easy to fall into local optimum, this paper proposes an improved heuristic ant colony algorithm for UAV route planning. In the early stage of the algorithm, Dijkstra algorithm is used to initialize the track, and heuristic information is introduced to improve the search efficiency; Logistic chaotic map is used to initialize pheromone to increase the diversity of solutions and improve the convergence speed of the algorithm; in the middle and late stage of the algorithm, multi track selection strategy and simulated annealing mechanism are used to improve the global search ability of the algorithm and avoid falling into local optimum due to too fast convergence speed Solution. The simulation results show that in the complex environment with threats and obstacles, compared with the basic ant colony algorithm, the improved ant colony algorithm can effectively plan a path from the start to the end, and has higher optimization accuracy and faster convergence speed, which has a certain application value.