摘要
提出了一种静态环境下机器人路径规划的改进蚁群算法。该算法使用栅格法对机器人的工作空间进行建模,通过模拟蚂蚁的觅食行为,采用折返的迭代方式对目标进行搜索。在搜索过程中,以移动方向一定范围内最大信息素和目标引导函数作为启发式因子。此外,根据蚁群算法处理本问题时信息素散播的特点,重构了信息素的更新策略和散播方式。仿真实验结果表明,这些改进措施使最优路径的寻找快速而高效,即使在障碍物非常复杂的环境下,也能迅速地规划出一条最优路径。
An improved ant colony algorithm was provided in this paper for robot path planning in a stalk environment. In this algorithm the model of robot＇s workspace was established with grid method and foldback iterating was used to search the aims by simulating the foraging behavior of ant colony. A heuristic factor based on the most pheromone in a moving direction range and a goal guiding function were used during the searching process. Furthermore, according to the features of the pheromone strewing when solving the problem by ant colony algorithm, the strewing method and updating strategy of pheromone were reconstructed. The simulation results show that these improvements make searching of the best path rapid and efficient. With this method a best path can be found rapidly even if the obstacles are exceedingly complicated.
出处
《计算机应用》
CSCD
北大核心
2008年第11期2877-2880,共4页
journal of Computer Applications
基金
宁夏自然科学基金资助项目(NZ0697)
关键词
蚁群算法
栅格模型
路径规划
ant cohmy algorithm
grid model
path planning
