Abstract: In view of the shortcomings of traditional ant colony algorithm are searching unreasonable, the converge slowly and getting into local solutions easily for the differential concentration of pheromone is small and the positive feedback have not obvious effect. The potential force of artificial potential field algorithm can guide the robot to the goal position, a kind of ant colony optimization with potential field heuristic, modeling the environment with grid method, the potential force, the potential force-influence coefficient and the distance between the robot and the goal are utilized to construct comprehensive heuristic information, ant colony algorithm mechanism is used to search a path in an unknown environment, many simulation results show ant colony optimization with potential field heuristic can find a shorter path and the converges fast.

Key words: artificial potential, ant colony algorithm, robot, path planning

摘要： 针对蚁群算法路径规划初期信息素浓度差异较小，正反馈作用不明显，路径搜索存在着盲目性、收敛速度相对较慢、易陷入局部最优等情况，人工势场算法的势场力可引导机器人快速朝目标位置前进，提出势场蚁群算法，通过栅格法对机器人的工作环境进行建模，利用人工势场中的势场力、势场力启发信息影响系数及蚁群算法中机器人与目标位置的距离构造综合启发信息，并利用蚁群算法的搜索机制在未知环境中寻找一条最优路径。大量的仿真实验表明势场蚁群算法路径规划能找到更优路径和收敛速度更快。

关键词: 人工势场算法, 蚁群算法, 移动机器人, 路径规划