基于势场蚁群算法的移动机器人全局路径规划方法

针对移动机器人路径规划蚁群算法收敛速度慢和人工势场法易陷入局部最优的问题,提出一种以栅格地图为环境模型,在蚁群算法搜索过程中加入针对具体问题的人工势场局部搜索寻优算法,将人工势场法中力因素转换为局部扩散信息素,使蚁群倾向于具有高适应值的子空间搜索,减少了蚁群算法在盲目搜索路径过程中产生的局部交叉路径及蚂蚁"迷失"数量,提高了蚁群对障碍物的预避障能力。对不同参数组合下2种算法及其它改进算法仿真结果做了比较,验证了基于势场蚁群算法的全局路径规划能够加快寻优过程且具有较强的搜索能力,收敛速度提高近1倍。

Robot Global Path Planning Based on Ant Colony Optimization with Artificial Potential Field

To solve the problems of the slow convergence speed in ant colony algorithm and the local optimum in artificial potential field method, an improved ant colony optimization algorithm was proposed for path planning of mobile robot in the environment expressed by the grid method. The local force factor of artificial potential field was converted into spreading pheromones in the ant searching process, so the ant colony algorithm focused on subspace search with high fitness. It reduced the partial cross paths and the number of lost ants in the process of general ant colony algorithm in blind search. It also enhanced the ability of robot to avoid obstacle in advance. Two algorithms simulation results under different parameter combinations showed that the improved ant colony algorithm not only solved the local optimum problem of artificial potential method, but also avoided the blind search of general ant colony algorithm. In addition, the simulation results were compared with other improved algorithms. The comparisons verified the efficiency of the proposed algorithm which shows better search performance and stronger searching ability than the traditional ant colony algorithms and other improved algorithms. The convergence speed of the proposed algorithm was nearly doubled.