Abstract: Ant colony algorithm for mobile robot under grid environment is defective in slow running, many broken lines, frequent turning points and local optimum. In order to obtain optimum path, this paper presents the inertia ant algorithm. Based on the initial path planned by traditional ant colony algorithm, the inertia principle is used to traverse all the nodes on initial path, deleting intermediate node when there is no obstacle existing between the two nodes and changing for optimum path. On the basis of path information, dynamically adjusting the pheromone evaporation coefficient, it can improve environmental adaptation performance of ant colony algorithm. The simulation results show that the inertia ant algorithm can quicker find the optimum path, and it can effectively optimize path quality.

Key words: inertia ant algorithm, path planning, pheromone evaporation coefficient

摘要： 栅格环境下蚁群算法规划出的移动机器人路径存在运行慢、路径弯多、转折次数多、局部最优等问题。为获得较优路径，提出了惯性蚁群算法。在传统蚁群算法规划的路径上，采用惯性优化原理，对每一个节点进行遍历，当两个节点间的优化路径上无障碍物时，将中间节点删除，换成优化路径。根据优化信息，动态调整信息素挥发系数，提高了算法环境适应能力。仿真结果表明，相比传统蚁群算法，惯性蚁群算法能更快地找到较优路径，能有效优化路径质量。

关键词: 惯性蚁群算法, 路径规划, 信息素挥发系数