Ant colony algorithm is an intelligent optimization algorithm, which has the advantages of strong robustness, accurate feedback information and strong distributed computing ability.It is widely used in mobile robot path planning.The original algorithm converges slowly and is prone to falling into local optimization.To solve the problems, an improved ant colony path planning algorithm is proposed.Firstly, the solution set is updated and mutated by integrating the idea of brainstorming, so as to speed up the convergence and ensure the diversity of the algorithm.Secondly, the local path attention mechanism is used to extract better path segments, so as to improve the optimization efficiency, and the adaptive t-distribution is added to the pheromone attention mechanism, so as to avoid falling into local optimization.The new pheromone updating method can promote the global search of the algorithm and ensure the convergence rate of the algorithm.Finally, simulation experiments in static environment are carried out in Matlab software, which have verified the effectiveness and feasibility of this algorithm.