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.
Path Planning Ant Colony Algorithm Predictive Learning Adaptive Adjustment
Yi Shen Mingxin Yuan Yunfeng Bu
Department of Mechanical Engineering, Huaiyin Institute of Technology, Huaian 223003, China School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
3030-3035
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）