With the development of the times, in order to make robots more autonomous, this has led to research on autonomous navigation and other aspects. In the research of autonomous navigation, it is generally divided into two aspects. Firstly, the construction of the map requires the combination of sensors and algorithms to construct the map. During the construction process, many factors need to be considered to apply different algorithms in different scenarios and robot hardware facilities. When not applicable, it is easy to experience map misalignment and other phenomena. After building the map, the robot needs to plan its path based on the current environment to reach the desired location. For path planning, Ant colony optimization algorithms with high robustness is used to better adapt to the environment. Compared with other algorithms, Ant colony optimization algorithms has better effect in indoor small environment. On the basis of the traditional algorithm, Ant colony optimization algorithms is improved by adjusting the volatilization coefficient of Pheromone, which makes the path planning of Ant colony optimization algorithms more reasonable.