基于蚁群算法的移动机器人路径规划  

Mobile Robot Path Planning Based on Ant Colony Optimization

作　　者：张晓玲[1] 罗印升[1] 张宝峰[2] 王亚春[2] 

机构地区：[1]江苏理工学院电气信息工程学院,江苏常州213001 [2]天津理工大学,天津300384

出　　处：《激光杂志》2016年第11期80-83,共4页Laser Journal

基　　金：国家自然科学基金项目(61305123)

摘　　要：采用蚁群算法(ACO)对自主移动的室内漫游机器人进行路径规划。首先通过栅格法对机器人所处的静态已知环境中的障碍物进行模拟,建立室内移动机器人的运动空间模型;然后采用蚁群算法,为机器人规划出从起始点到终点的最优或近似最优的运动轨迹,即全局路径规划;机器人上安装红外传感器和视觉传感器,能够按着规划出的轨迹自动导航,自行规避障碍运动到达目标点。实验结果表明了此算法快速性、简捷性、有效性及高效性,克服了大多数传统算法容易陷入局部极小值问题,能迅速规划出较优的路径。Ant colony optimization（ACO） is adopted for path planning of autonomous mobile robot indoor. At first the stoical and global environment known is abstracted with grid method before we build the workspace model of the robot. With the ant colony algorithm, the robot tries to find a path which is optimal or optimal-approximate path from the starting point to the destination. The robot with the built-in infrared sensors navigates autonomously to avoid collision the optimal path which has been built, and moves to the object. Based on the MATLAB platform, the simulation resuits indicate that the algorithm is rapid, simple, efficient and high-performance. Majority of traditional algorithms of the path planning have disadvantages, for instance, the method of artificial potential field is falling into the problem of local minimum value easily. ACO avoids these drawbacks, therefore the convergence period can be extended, and optimal path can be planned rapidly.

关 键 词：蚁群算法 栅格法 机器人 路径规划 

分 类 号：TN209[电子电信—物理电子学]