基于改进蚁群算法的无人机三维航迹规划

针对传统蚁群算法在无人机三维航迹规划中，存在早期盲目搜索、收敛速度慢、易陷入局部最优等问题，本文提出了一种改进蚁群算法。该算法利用空间位置初始化信息素分布并设定浓度阈值，增强了蚁群早期搜索的方向性，避免了算法陷入局部最优；设计兼顾距离因素和方向因素的启发函数，改善了航迹规划质量；采用自适应挥发因子控制信息素的挥发，提高了算法的收敛速度。通过两组实验表明，该算法相比传统算法规划的航迹平均长度均减少18.6%，平均迭代次数分别减少63.3%和78.7%，平均拐角次数分别减少62.5%和42.3%。

3D path planning of UAV based on improved ant colony algorithm

Aiming at the problems of early blind search, slow convergence and easy to fall into local optimum in the traditional ant colony algorithm for UAV 3D path planning, an improved ant colony algorithm is proposed in this paper. The algorithm uses spatial location to initialize the pheromone distribution and set a concentration threshold, which enhances the directionality of the early search of the ant colony and avoids the algorithm from falling into the local optimum. The heuristic function which takes into account both distance and direction factors is designed to improve the quality of path planning. The adaptive volatility factor is used to control the volatility of the pheromone, which improves the convergence speed of the algorithm. Compared with the traditional algorithm, two experiments show that the proposed algorithm reduces the average path length by 18.6%, the average iteration times by 63.3% and 78.7%, and the average corner times by 62.5% and 42.3%, respectively.