Aiming at the problems of low search efficiency, slow convergence rate, and local optimal solution when a basic ant colony algorithm is applied to mobile robot path planning under a 2D static grid map, an adaptive mechanism improved ant colony algorithm for the path planning of mobile robots under the two-dimensional grid map environment is proposed. Firstly, pseudo-random state transition rules are used. For path selection, a dynamic selection factor is defined to adaptively update the selection ratio and a distance parameter is introduced to calculate the transition probability. Hence, the global search ability and search efficiency of the algorithm is improved. Then, based on the maximum and minimum ant model and the elite ant model, a reward and punishment mechanism is proposed to update the pheromone increment. Finally, a pheromone dynamic volatilization factor is defined to limit the upper and lower range of the pheromone concentration to improve the convergence speed of the algorithm and the global search ability. The global path planning comparison experiments of the mobile robot are carried out under two-dimensional static grid maps of different specifications, and the experimental results show that the adaptive mechanism improved ant colony algorithm has a faster convergence speed and significantly improved search efficiency, and it has a better global search ability, which verifies the effectiveness and superiority of the algorithm.