Objective In robot path planning the traditional ant colony algorithm is found to have poor convergence and produce excessive bends. Additionally it updates pheromones based on the overall information of the paths traversed by ants leading to the neglect of some potentially superior solutions within those paths. To address this issue an improved ant colony algorithm integrating multiple strategies is proposed. Methods First a dual-operator collaborative strategy was adopted. Based on the traditional ant colony system ACS the calculation formula for the ant transition probability was improved. The angle guidance operator and the bend suppression operator were used to increase the goal-directedness of path search and reduce the number of path bends. Second the strategy of an adaptive pheromone evaporation coefficient was utilized to adjust the pheromone evaporation coefficient. This enabled the algorithm to better balance the convergence speed and the search ability. Finally to further enhance the algorithm?? s performance a secondary path planning strategy was employed. A secondary search was conducted on the random local paths of the planned path to obtain a better composite path. Results Comparison of different algorithms on 20×20 and 30×30 maps showed that the improved ant colony algorithm with multiple strategies produced paths with shorter length smaller cumulative turning angle and better iterative convergence. Conclusion The improved ant colony algorithm with multiple strategies has good performance in robot path planning.