Path planning of mobile robot based on improved ant colony optimization algorithm

Path planning for mobile robots is a critical research area in intelligent control and automation. To address the issues of slow convergence and susceptibility to local optima in traditional ant colony algorithms within complex environments, this paper introduces a series of improvement strategies. An attenuation function is incorporated to optimize heuristic information, enabling ants to rely more on pheromone-based decision-making in the later stages. Directional guidance angles are added to enhance the directivity of state transition probabilities, accelerating the path search process. Additionally, cubic B-spline curves are employed to smooth the paths, while the 2-opt algorithm is integrated to further minimize path lengths. These enhancements significantly improve the algorithm's efficiency and stability. Simulation results validate the superiority of the proposed algorithm in path planning performance, offering an effective solution for optimizing mobile robot navigation in complex environments.