基于量子遗传算法的移动机器人的一种路径规划方法

Path Planning Method of Mobile Robot Based on Quantum Genetic Algorithm

以人工势场法和栅格法为基础,考虑到遗传算法的"收敛速度慢"和"早熟收敛"问题,提出了一种基于量子遗传算法的机器人路径规划方法。该方法采用栅格法进行路径规划,利用人工势场法控制移动机器人,利用量子遗传算法选择最优或次优个体,并且引入双适应度评价函数评价进化个体,为最优或次优个体进入下一代提供了保障。仿真实验表明,该方法的寻优能力及稳定性均优于遗传算法和量子遗传算法,且具有更好的收敛性以及更强的连续空间搜索能力,适于求解复杂优化问题。

Based on artificial potential field and grid method,in order to solve the prematurity and lower convergence speed in genetic algorithm（GA） for robotic path planning,a novel mobile robot path planning method based on quantum genetic algorithm（QGA） was proposed.This method uses grid method to establish mobile robot work environment model,artificial potential field to control mobile robot,quantum genetic algorithm to select the optimal or sub-optimal path,and double fitness evaluation function to evaluate the path to protect the optimal or sub-optimal path in to the next generation.The ability of finding the best solution and the stability of this method are greatly improved compared with GA and QGA by Simulation,and it has better convergent property and ability of searching more extensive space.It is fit for the solution of complex optmization problems.