基于改进蚁群算法的移动机器人路径规划  

Mobile robot path planning based on improved ant colony algorithm

作　　者：赵静 汤云峰[1,2] 蒋国平 徐丰羽 丁洁[1,2] ZHAO Jing;TANG Yunfeng;JIANG Guoping;XU Fengyu;DING Jie(College of Automation&College of Artificial Intelligence,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;Jiangsu Engineering Lab for IoT Intelligent Robots,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)

机构地区：[1]南京邮电大学自动化学院、人工智能学院,江苏南京210023 [2]南京邮电大学江苏省物联网智能机器人工程实验室,江苏南京210023

出　　处：《南京邮电大学学报（自然科学版）》2019年第6期73-78,共6页Journal of Nanjing University of Posts and Telecommunications：Natural Science Edition

基　　金：江苏省“六大人才高峰”(JY-081)资助项目

摘　　要：为了解决蚁群算法在移动机器人路径规划应用中的不足,通过改进启发函数和信息素挥发因子来提升算法的性能。首先,利用栅格法的便捷性对环境进行建模,对每个栅格进行标记,使蚁群从初始栅格移动到目标栅格进行路径搜索;然后,构造新的启发函数以提高蚂蚁搜索的目的性及算法的运算速度;最后,使挥发因子自适应变化,保证蚂蚁在全面地搜索路径时也能够快速收敛。多次实验的结果证明,在有较多障碍物的情况下,改进的算法能够帮助移动机器人快速寻找到最优路径。To solve the problems of an ant colony algorithm for mobile robot path planning,the heuristic function and the pheromone volatilization factor are used to improve the performance of the algorithm.Firstly,the grid method is very convenient for modeling the working environment of the mobile robot.Each grid is marked and ant colonies move from the initial grid to the target grid for path search.Then,a new heuristic function is constructed to improve the ant search and the operation speed of the algorithm.Finally,the volatilization factor is adaptively changed to ensure that ants can quickly converge when comprehensively searching the path.Experimental results prove that in case of more obstacles,the improved algorithm can help the mobile robot to find the optimal solution quickly.

关 键 词：蚁群算法 机器人 自适应 启发函数 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]