For mobile robots in path planning, there exist problems such as blind search in the initial stage, excessive number of turns, poor path smoothness, and easy trapping in local optimum. A robot path optimization strategy based on multi-objective constraints is proposed. This strategy is an improvement on the traditional ant colony algorithm. Firstly, the A* algorithm is introduced to enhance the pheromone concentration of adjacent feasible paths, reducing the blindness of the initial search and improving the convergence speed of the algorithm. Secondly, the distance heuristic function is improved by adding the distance relationship between the candidate node and the target node to enhance the guidance of the target node to the global optimal path. Thirdly, a turning heuristic function is proposed. This function is composed of the obstacle concentration constraint function, the turning constraint function, and the smoothness constraint function. The turning heuristic function is added to the transition probability formula to guide ants to move towards paths with fewer turns, shorter distance and smoother. Finally, the pheromone evaporation factor is improved, effectively balancing the weights of local search and global search. The pheromone update rule is also improved, and a reward and punishment mechanism is set for pheromone update from two aspects of path length and the number of turns, reducing the redundancy of pheromone and improving the algorithm efficiency. Meanwhile, the pheromone concentration is limited to prevent the algorithm from falling into premature problems. Simulation experiments show that under the same experimental conditions, compared with other algorithms, the improved algorithm has a shorter running time, a shorter optimal path length, a smoother path, and a faster convergence speed.