移动机器人路径规划的改进蚁群优化算法

摘要

An improved ant colony optimization algorithm- a differential evolution chaos ant colony optimization(DEACO) algorithm is proposed to plan the optimal collision-free path for a mobile robot in a complicated statc environment. It uflizes differential evolution algorithm to update the pheromone, and appends the chaos disturbance factor in the updating process to avoid the possible stagnation phenomenon. Finally, a new evaluation criterion is employed to enhance the escaping capability of algorithm, avoid the path-locked situations and improve the efficiency in planning the optimal path. Simulation results indicate that an optimal and safe path which the robot moves on can be rapidly obtained even in a complicated geographical environment. The results are very satisfactory.%针对蚁群算法易陷入局部最优的缺点,提出了一种复杂静态环境下移动机器人路径规划的改进蚁群优化算法一差分演化混沌蚁群算法.该算法利用差分演化算法进行信息素的更新,同时对可能出现的停滞现象,在信息素更新时加入了混沌扰动因子,算法还采用了一个新的评价函数;从而增强了算法的逃逸能力,避免了路径死锁现象,也提高了最优路径的搜索效率.仿真结果表明:即使在障碍物非常复杂的环境,本算法仍能快速规划出安全的优化路径.效果令人满意.