Research on Mobile Robot Path Planning in Angle-Guided Ant Colony Optimization Algorithm

Abstract

Addressing the issues of slow convergence, low smoothness, and long computation time in the basic Ant Colony Optimization algorithm, this paper proposes a novel variant of the Ant Colony Optimization algorithm. The proposed algorithm improves the heuristic function by introducing angle guidance. It incorporates the idea of the estimation function from the A* algorithm to enhance the direction toward the target point. Simultaneously, the information pheromone update rule is improved, with increased pheromone concentration on the optimal paths and reduced concentration on the worst paths, while introducing a dynamic pheromone evaporation factor. These enhancements increase search effectiveness and quality while also speeding up algorithm convergence. To test the efficacy of the proposed algorithm, the authors conducted simulation comparisons with three existing algorithms. The experimental findings show that the suggested method is capable of producing pathways with higher smoothness, fewer turns, and faster convergence.