Path Planning of Mobile Robot Based on Dynamic Chaotic Ant Colony Optimization Algorithm

Abstract

In this paper, a Dynamic Chaotic Ant Colony Optimization (DCACO) algorithm is proposed to solve the problems of traditional Ant Colony Optimization (ACO) algorithm in mobile robot path planning, such as long time consuming, slow convergence speed and easy to fall into local optimum. In DCACO, cosine annealing strategy is used to improve the expectation heuristic factor to balance the global search ability and convergence speed of the algorithm. In addition, this paper proposes a dynamic chaotic ant colony system, whose core is that improved logistic chaotic operator disturbs pheromone update in the early stage of iteration to avoid falling into local optimization, and is eliminated in the later stage to ensure the convergence speed. The experimental results show that this algorithm is effective and superior in path searching performance and convergence speed compared with the existing state-of-the-art algorithms.