Study on adaptive planning strategy using ant colony algorithm based on predictive learning

To solve the path planning in the complicated environments, a new adaptive planning strategy using ant colony algorithm (AACA) based on predictive learning is presented. A novel predictive operator for direction during the ant colony state transition is constructed based on an obstacle restriction method (ORM), and the predictive results of proposed operator are taken as the prior knowledge for the learning of the initial ant pheromone, which improves the optimization efficiency of ant colony algorithm (ACA). To further solve the stagnation problem and improve the searching ability of ACA, the ant colony pheromone is adaptively adjusted under the limitation of pheromone. Compared with the corresponding ant colony algorithms, the simulation results indicate that the proposed algorithm is characterized by the good convergence performance on pheromone during the path planning. Furthermore, the length of planned path by AACA is shorter and the convergence speed is quicker.