Abstract: This paper mainly researches the problem of path planning in navigation of robot. The algorithm describes the environment of robot making use of grid division. In this environment, Ant Colony Optimization(ACO) is used to do path planning. The ACO is improved by the idea of Genetic Algorithm(GA), GAA algorithm is presented. Simulation results show that the method can effectively improve the speed of path searching, path optimizing and path smoothness.

Key words: Ant Colony Optimization(ACO), GAA algorithm, path planning

摘要： 研究机器人导航中的路径规划问题，运用栅格法和图论思想建立环境模型，在该模型中通过蚁群算法进行路径寻优，提出用遗传算法的思想改进已有蚁群算法，即GAA算法。仿真实验结果表明，该算法能有效地提高机器人的路径搜索速度及路径优化、路径平滑等方面的指标。

关键词: 优化蚁群算法, GAA算法, 路径规划

CLC Number: 

• TP24