增强蚁群算法的机器人最优路径规划

Path planning optimization based on reinforcement of artificial potential field

为解决复杂环境中机器人最优路径规划问题,本文结合增强学习和人工势场法的原理,提出一种基于增强势场优化的机器人路径规划方法,引入增强学习思想对人工势场法进行自适应路径规划.再把该规划结果作为先验知识,对蚁群算法进行初始化,提高了蚁群算法的优化效率,同时克服了传统人工势场法的局部极小问题.仿真实验结果表明,该方法在复杂环境中,对机器人的路径规划效果令人满意.

In order to solve the problem of optimal path planning for robot in complex environment, a path planning method based on the artificial potential field optimization is proposed in this paper. The ant algorithm is initialized by the planning result of the artificial potential field reinforcement as the prior knowledge, which improves the algorithm＇s efficiency. On the other hand, the local minima problem in the artificial potential field method is solved successfully. The result of simulation shows that the method in this paper works well in solving the relevant problems.