Robot Path Planning Based on Grid Map Using Improved Ant Colony Algorithm

Abstract

For the problems of slow convergence and easy to fall into local optimum of traditional ant colony algorithm (ACO) for robot path planning, An improved ant colony algorithm (IACO)based on grid map is proposed in this paper. Firstly, in order to improve the positive feedback ability of pheromone in the later period, an adaptive adjustment factor is introduced into the heuristic function. Secondly, the rotation function is introduced into the pheromone state transition probability to balance the relationship between path length and angle. Finally, in order to ensure the quality of participating pheromone update nodes, local optimization strategies are designed based on cross optimization and redundant point deletion, and different quality paths are updated with pheromone difference mechanism to achieve convergence of high-quality nodes. The experimental results show that IACO can make the robot obtain the global optimal path, and it will have good stability and environmental adaptability.