针对传统蚁群算法（ant colony algorithm, ACO）在移动机器人路径规划中存在的环境适应性差、拐点个数多、计算复杂度高等问题，提出一种基于Sigmoid统计迭代的蚁群算法。首先，采用Sigmoid激活函数分布策略，增加起点到目标点路线上信息素的初始浓度，降低算法前期搜索的盲目性；其次，引入自适应因子动态调节启发函数，增加蚂蚁选择全局最优节点的期望程度，降低算法的收敛时间；最后，在每代蚁群中进行统计分析，提取每代蚂蚁路径最优、最差、平均三个特征参数，并根据迭代次数动态调整信息素更新函数。仿真结果表明，本文改进算法与蚁群系统、精英排序算法、传统蚁群算法相比，最优路径长度分别缩短2.7%、3.2%、5.4%，最优路径次数分别增加42%、53%、62%，最差路径长度分别缩短49%、62%、73%。研究显示，本文改进算法具有更强的全局寻优能力和较好的应用价值。

The sigmoid iteration ACO(ant colony algorithm) was optimized for the problems of poor environmental adaptability, high number of inflection points and high computational complexity that exist in the traditional ACO(ant colony algorithm) in route planning. Firstly, the Sigmoid activation function distribution strategy was adopted to improve the initial pheromone spread through the position of the mesh nodes, and the initial concentration of the pheromone was assigned by the sigmoid, which reduced the blindness of the algorithm's pre-search. Secondly, the adaptive factor was introduced to dynamically regulate the heuristic function, which increased the degree of expectation of the ants in choosing the globally optimal node, and reduces the convergence time of the algorithm. Lastly, a statistical analysis was carried out in each generation of the ant, and the three characteristic parameters of ant path optimal, worst and average were extracted in each generation, and the pheromone updating function was dynamically adjusted according to the number of iterations to give full play to the parallelism characteristics of the algorithm. The results prove that the improved algorithm shortens the optimal path length by 2.7%, 3.2%, and 5.4%, reduces the average number of iterations by 42%, 53%, and 62%, and shortens the worst path length by 49%, 62%, and 73%, respectively, when compared with the ant colony system, the elite ranking algorithm, and the traditional ACO. The study prove that the optimized algorithm has stronger global optimality seeking ability and better application value.