Mobile robot path planning based on hybrid ant colony optimization

Abstract

To solve the problems of the ant colony optimization (ACO), such as slow convergence speed, easy to fall into local extremum and deadlock in path planning, this paper proposed an improved ACO, which was hybridized by PSO based on logistic chaotic mapping, called hybrid ant colony optimization (HACO). According to the number of obstacles around the next feasible node, HACO distributes the initial pheromones unevenly to avoid the ant getting stuck in deadlock. According to the orientation of the next node selected by the ant, the heuristic information is adaptively adjusted to guide the ant to the direction of the target position. When updating the pheromone, the local and global search mechanism of the particle swarm optimization is used to improve the pheromone update rule and accelerate convergence speed. Finally, the grid method is used to construct the environment map, and simulation experiments are conducted in different environments. The experimental results verify the effectiveness and feasibility of the improved algorithm.