Logistics Robot Path Planning Based on Adaptive Ant Colony Algorithm

Abstract

As for traditional ant colony algorithm using in mobile robot path planning, there are problems such as the convergence speed and searching ability existing unbalance, falling into local optimum easily and so on. An adaptive ant colony algorithm is proposed. To improve the global search capability of the ants, pseudo-random state transfer rules are used, while angle-inspired information is introduced into the state transfer probabilities, improving the distance heuristic information function to enhance the directionality of algorithm search; The global pheromone update mechanism and pheromone volatility coefficients are redesigned. The adaptive pheromone volatility coefficient update mechanism is proposed, which dynamically adjusts the pheromone volatility coefficient adaptation algorithm according to the number of iterations, so that the algorithm also maintains a strong global search capability at a later stage. By comparing this paper's algorithm with other algorithms through simulation experiments, the global search ability and convergence speed of this paper's algorithm are significantly improved.