Research on Global Path Planning of Robot Based on Ant Colony Algorithm and Gaussian Sampling

Abstract

In response to the issue of the traditional ant colony algorithm (T-ACO) with many iterations and slow convergence speed in robot global path planning, we propose an enhanced ant colony algorithm (S-IACO) that incorporates Gaussian sampling. Firstly, the initial pheromone concentration contained in the raster map is preprocessed. Gaussian distribution sampling is adopted, and the sampling median value is used as the initial pheromone concentration of the raster map. Secondly, the heuristic function of the ant colony algorithm was improved. The number of ant colony iterations and the current path length were dynamically introduced into the heuristic function of the algorithm as influencing factors. Finally, this work redefined the pheromone update rule and introduced the concept of loss function by considering the influence of initialization pheromone on the results. By comparing with other improved ant colony algorithms, as well as PSO and GA algorithms, the S-IACO algorithm proposed has fewer iterations and faster convergence speed.