基于势场导向的对数蚁群算法的移动机器人路径规划

针对蚁群算法存在收敛速度慢,易陷入局部最优的问题,提出了一种将人工势场和对数蚁群算法相融合的新算法.该算法是在蚁群算法的基础上,将势场的影响因素引入到蚁群算法的状态转移概率函数和启发函数中,并通过对数函数模型对蚁群算法的信息素更新策略进行改进,使得路径算法搜索不再具有盲目性,并加快算法的收敛速度.为了验证改进算法的有效性,分别在不同环境的2维栅格地图中进行仿真.仿真结果表明,相比改进前的蚁群算法,改进后的蚁群算法在路径规划中收敛速度更快,规划效率更高.将基于势场导向的对数蚁群算法应用于Hokuyo激光建图的实际机器人上进行路径规划实验.实验结果表明,改进后的蚁群算法路径搜索效率较改进前提高了约52％.

Path planning of mobile robot based on potential field oriented logarithmic ant colony algorithm

In order to solve the problem that the ant colony algorithm has slow convergence speed and easily falls into the local optimal, we propose a new algorithm combining artificial potential field and logarithmic ant colony algorithm. Based on the ant colony algorithm, the influence factors of potential field is introduced into the transition probability function and heuristic function of the ant colony algorithm, and the pheromone updating strategy of ant colony algorithm is improved by the logarithmic function model, which makes the path search algorithm no longer blind, and accelerates the convergence speed of the algorithm. In order to verify the effectiveness of the improved algorithm, simulation is carried out in 2d grid maps in different environments. The simulation results show that compared with the ant colony algorithm before the improvement, the improved ant colony algorithm has faster convergence speed and higher planning efficiency in path planning. Finally, the potential field-oriented logarithmic ant colony algorithm is applied to a real robot using Hokuyo laser to make a comparison experiment. The experimental results show that the efficiency of the improved ant colony algorithm is about 52% higher than that of the improved ant colony algorithm.