摘要
随着物流行业的快速发展,物流机器人已经成为物流行业的重要部分。针对传统蚁群算法在实际物流机器人工作中存在算法初期具有盲目性、寻找路径过长、收敛速度过慢及转弯次数过多的问题,提出了一种改进蚁群算法。该算法通过与A*算法相结合来对初始信息素浓度进行定义,避免了算法初期的盲目性;针对算法收敛速度慢的问题,对挥发因子作出了修正,同时引入转弯因子,使得到的路径更加平滑。首先,通过A*算法得到一条次优解路径,将该路径周围节点的初始信息素浓度按照线性递减的方式进行定义;其次,在信息素更新过程中,综合考虑蚂蚁所走路径长度和转弯次数,对路径节点上的信息素进行更新;最后,提出了一种随迭代次数变化的动态挥发因子,提高了物流机器人的工作效率。为了验证改进蚁群算法的可靠性,在20×20、30×30的地图上进行了对比,实验结果证明改进蚁群算法具有实际价值。
With the rapid development of logistics industry,logistics robot has become an important part of logistics industry.Aiming at the problems of blindness in the initial stage of the algorithm,long search path,slow convergence speed and too many turns in the traditional ant colony algorithm in the actual work of logistics robot,an improved ant colony algorithm was proposed.The algorithm defined the initial pheromone concentration through the combination of A*algorithm,which avoids the blindness in the initial stage of the algorithm.Aiming at the slow convergence speed of the algorithm,the volatilization factor was modified and the turning factor was introduced to make the obtained path more smooth.Firstly,a suboptimal solution path was obtained by A*algorithm,and the initial pheromone concentration of nodes around the path was defined in a linear decreasing manner.Secondly,in the process of pheromone updating,the pheromone on the path node was updated by comprehensively considering the path length and turning times of the ant.Finally,a dynamic volatilization factor varying with the number of iterations was proposed to improve the work efficiency of logistics robot.In order to verify the reliability of the improved ant colony algorithm,a comparison was made on 20×20 and 30×30 maps.The experimental results show that the improvement has practical value.
作者
宋宇
张浩
程超
SONG Yu;ZHANG Hao;CHENG Chao(School of Computer Science and Engineering,Changchun University of Technology,Changchun 130012,China)
出处
《现代制造工程》
CSCD
北大核心
2022年第11期35-40,47,共7页
Modern Manufacturing Engineering
基金
吉林省教育厅重点项目(JKH20210754KJ)。
关键词
物流机器人
路径规划
蚁群算法
A*算法
logistics robot
path planning
ant colony algorithm
A*algorithm