Abstract: An improved ant colony algorithm in narrow channel environment is proposed which solves the deficiency of the traditional ant colony algorithm such as local optimum and ant “lost”. On the one hand, random points evenly distributed on the plane are randomly removed from obstacles. Those points concentrate in the narrow channel which are utilized to construct heuristic information. And heuristic information is used to improve the capability to cross the narrow channel. On the other hand, sharp corners strategy is used to simulate the flight character. The results demonstrate that improved ant colony algorithm shows better search performance and lesser ant “lost”.

Key words: ant colony algorithm, information pheromones, narrow channel environment, sharp corners strategy

摘要： 在全局静态环境下，提出了一种适用于窄通道环境路径规划的蚁群算法。解决了传统蚁群算法容易陷入局部最优解、易于造成蚂蚁迷失等不足。一方面，在灰度矩阵上随机撒点并将障碍物内的节点随机移动，使得窄通道内节点密度提高，并以此为启发信息素，提高了无人飞行器穿过窄通道的能力，减少了蚂蚁迷失现象。另一方面，引入了无人飞行器轨迹的尖角优化策略，更好地模拟了无人飞行器的飞行特征。结果表明：新的算法所获取的最优路径具有更好的全局搜索能力，并且造成了较少数量的蚂蚁迷失。

关键词: 蚁群算法, 信息素, 窄通道环境, 尖角优化策略