基于改进蚁群算法的大棚移动机器人路径规划研究  

Research on Path Planning of Greenhouse Mobile Robot Based on Improved Ant Colony Algorithm

作　　者：梁秋阳 王影[1] 刘麒[1] 夏春燕 LIANG Qiuyang;WANG Ying;LIU Qi;XIA Chunyan(School of Information and Control Engineering,Jilin Institute of Chemical Technology,Jilin City 132022,China)

机构地区：[1]吉林化工学院,吉林吉林132022

出　　处：《长江信息通信》2024年第12期81-83,共3页Changjiang Information & Communications

摘　　要：针对大棚移动机器人路径规划存在搜索时间较长、效率较慢等问题,提出了一种基于改进蚁群算法的路径规划方法。在传统蚁群算法的启发函数基础上,为平衡算法的全局搜索性能和收敛速度,引入自适应调整因子。在状态转移概率公式中,为防止初期易进入局部最优,引入稳定因子。在信息素方面,在精英蚂蚁系统中引入了一种动态调整的增强因子,加强了那些有可能成为最优路径的边,从而实现更快、更准确的收敛。实验结果表明,相较于传统蚁群算法,改进后的算法在迭代稳定次数均值、最优路径长度均值、转弯次数均值都有所降低,显著地提高了大棚移动机器人的工作效率。Addressing the challenges of long search times and slow efficiency in greenhouse mobile robot path planning,an improved ant colony algorithm-based approach is proposed.To balance global search performance and convergence speed,an adaptive adjustment factor is introduced,building upon the heuristic function of the traditional ant colony algorithm.In the state transition probability formula,a stability factor is introduced to prevent premature entrapment in local optima.Additionally,a dynamic adjustment enhancement factor is introduced in the elite ant system,emphasizing edges likely to lead to optimal paths,thus achieving faster and more accurate convergence.Experimental results demonstrate that compared to the traditional ant colony algorithm,the improved algorithm significantly reduces the mean values of stable iteration count,optimal path length,and turning frequency,significantly enhancing the work efficiency of greenhouse mobile robots.

关 键 词：大棚 移动机器人 路径规划 蚁群算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]