Path planning of mobile robot based on multi-strategy adaptive improved ant colony algorithm

Path planning is a critical technology for autonomous navigation in mobile robots and a primary focus in robotics research. The Ant Colony Optimization (ACO) algorithm is widely used for path planning. However, traditional ACO algorithms often suffer from slow convergence, low efficiency, and a tendency to get trapped in local optima. Thus, a multi-strategy adaptive improved ant colony algorithm (MSAACA) is proposed in this study. First, the initial pheromone is non-uniformly initialized based on the number of obstacles in adjacent nodes, and a new adaptive regulation of pheromone volatility is introduced. Secondly, by introducing the artificial potential field method and combining potential field attraction with distance information, the heuristic information function is enhanced. At the same time, the state transition rule is improved, and dynamic selection factors and dynamic adjustment factors are defined to update the selection ratio adaptively and dynamically adjust the influence of heuristic information, thus improving the global nature of the algorithm. Finally, the optimal path obtained by the algorithm is further optimized to reduce the number of inflection points effectively, leading to reduced running time and energy consumption of the mobile robot. The experimental results demonstrate that the algorithm exhibits high global search capability, significantly accelerated convergence speed, and improved working efficiency of mobile robots. This validates the effectiveness and superiority of the proposed algorithm.