基于改进蚁群算法的移动机器人路径规划研究

为解决传统蚁群算法收敛速度慢、极易陷入局部最优解的问题,文中提出了一种改进蚁群算法,并将其应用于移动机器人路径规划问题。蚁群算法的路径规划采用栅格法建立环境模型,并对障碍物进行扩大处理,从而有效降低了移动机器人在运动过程中与障碍物相碰撞的可能性;构造启发函数以降低蚁群搜索路径的长度;引入信息素扩散算法,并提高算法在初期的全局搜索能力,从而加快了算法的后期收敛速度。仿真结果表明,所提出的算法在收敛速度上比传统蚁群算法提高近一倍,可以规划出最优路径。

Research on Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

In order to solve the problem that the traditional Ant Colony (AC) algorithm had a slow convergence speed and was easy to fall into a local optimal solution, an improved AC algorithm was proposed and applied to the path planning problem of mobile robots. This method built the environment using the grid model, and the obstacles were expanded to effectively reduce the possibility of the moving mobile robot colliding with obstacles. By constructing the heuristic function, the length of the ant colony search path was reduced. The pheromone diffusion algorithm was used to improve the global search ability of the algorithm in the initial stage, so as to accelerate the late convergence speed of the algorithm. The simulation results showed that the proposed improved AC algorithm could plan the optimal path. Besides, compared to the traditional method, the proposed improved AC algorithm doubled convergence speed.