摘要
提出一种机器人路径规划的蚁群算法,该算法引入信息素限定和自适应信息素挥发系数的方法解决蚁群算法应用中的停滞现象和搜索能力的问题。算法仿真研究中发现了算法的收敛速度和环境地图建模的方式有密切关系,提出栅格地图模型的坐标变换法,提高了算法的运行效率。比较仿真实验结果证实了本算法的有效性和快速性。
A novel ant colony optimization （ACO） algorithm for robot path planning is presented. The method of pheromone restriction and adaptive volatile coefficient is proposed to solve the problem of algorithm stagnation and global searching ability in the process of traditional ACO application. The relation between the algorithm running rate and map modeling method is found in the simulation process of ACO, so the coordinate transformation of grid map modeling is proposed to improve the algorithm running rate. The comparative simulation results are shown the effectiveness and speediness of this algorithm.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2008年第5期952-955,共4页
Systems Engineering and Electronics
关键词
蚁群算法
路径规划
栅格地图建模
ant colony optimization
path planning
grid map modeling
