[1]马金科,王 直.基于改进蚁群算法的盘点型机器人路径规划[J].计算机技术与发展,2019,29(07):84-87.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 017]
　MA Jin-ke,WANG Zhi.Path Planning of Inventory Robot Based on Improved Ant Colony Algorithm[J].,2019,29(07):84-87.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 017]
点击复制

基于改进蚁群算法的盘点型机器人路径规划()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年07期
页码:
84-87
栏目:
智能、算法、系统工程
出版日期:
2019-07-10

文章信息/Info

Title:
Path Planning of Inventory Robot Based on Improved Ant Colony Algorithm
文章编号:
1673-629X(2019)07-0084-04
作者:
马金科; 王 直
江苏科技大学 计算机学院,江苏 镇江 212003
Author(s):
MA Jin-ke; WANG Zhi
School of Computing,Jiangsu University of Science and Technology,Zhenjiang 212003,China
关键词:
机器人; 路径规划; 蚁群算法; 参数自适应; 旅行商问题
Keywords:
robot; path planning; ant colony algorithm; parameter adaptation; traveling salesman problem
分类号:
TP242.6
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 07. 017
摘要:
针对盘点型机器人盘点物品过程中路径规划实时性和稳定性差的问题,以传统的蚁群算法为基础,提出了一种改进型的蚁群算法。 改进型的蚁群算法包括三点优化:第一是提出了自适应的挥发系数设置方法,即算法前期设置较小的挥发系数,减小蚂蚁间互相吸引;算法后期,挥发系数设置较大,提高算法收敛速度。 第二是对各个路径上的初始浓度做出调整,加大了起始点和终点连线附近的信息素浓度。 这样能较大提高前期搜索的速度。 第三是全局信息素更新时,按单次迭代出的路径长短,在较短路径上加强信息素浓度,在较长路径上削减信息素浓度。 研究结果表明,当对传统算法做出这三点优化后,改进后的算法不仅路径规划收敛的速度更快,效率更高,而且寻得的路径也更优更稳定。 经仿真实验验证,在类旅行商问题上,改进后的算法确实有更快的收敛速度,且能避免陷入局部最优解,而得到全局最优路线。
Abstract:
Aiming at the problem of poor real-time and stability of path planning in inventory process of inventory robots,we propose an improved ant colony algorithm based on traditional ant colony algorithm. It includes three-point optimization. The first is to propose an adaptive volatilization coefficient setting method which sets a small volatilization coefficient in the early stage to reduce mutual attraction between the ants,and in the later stage,sets a large volatilization coefficient to improve the convergence speed. The second is to adjust the initial concentration on each path to increase the pheromone concentration near the start and end points. This can greatly improve the speed of the prophase search. The third is the global pheromone update,the length of the path by a single iteration,the pheromone concentration is enhanced on the shorter path,and the pheromone concentration is reduced on the longer path. The research shows that when these three optimizations are made to the traditional algorithm,the improved algorithm not only converges faster and more efficiently,but also finds better and more stable paths. The simulation experiment proves that the improved algorithm has a faster convergence speed on the traveling salesman problem,and can avoid falling into the local optimal solution and get the global optimal route.

相似文献/References:

[1]熊力 方康玲 刘永祥.GPS导航系统在道路巡检中的应用研究[J].计算机技术与发展,2010,(06):246.
　XIONG Li,FANG Kang-ling,LIU Yong-xiang.Research of GPS Navigation System in Road Patrol Line[J].,2010,(07):246.
[2]胡佳 汪峥.工业机器人路径规划的双目标优化策略[J].计算机技术与发展,2009,(05):16.
　HU Jia,WANG Zheng.Bi- objective Optimization of Path Planning for Manipulators[J].,2009,(07):16.
[3]单承刚.嵌入式Linux下超声波避障系统设计[J].计算机技术与发展,2009,(06):29.
　SHAN Cheng-gang.Design of Ultrasonic Obstacle Avoidance System in Embedded Linux System[J].,2009,(07):29.
[4]郑延斌 李新源 段德全.一种保持Agent团队队形的路径规划方法[J].计算机技术与发展,2009,(07):159.
　ZHENG Yan-bin,LI Xin-yuan,DUAN De-quan.A Path Planning Algorithm with Agent Team Formation Maintained[J].,2009,(07):159.
[5]刘雁菲 邵晓东 李申.基于Vega的虚拟漫游场景中的路径规划研究[J].计算机技术与发展,2008,(06):9.
　LIU Yan-fei,SHAO Xiao-dong,LI Shen.Path Planning Based on Vega of Navigation in Virtual Environment[J].,2008,(07):9.
[6]陈虹 梁文彬 李宗宝 董航飞.基于机器人的神经网络预测控制算法[J].计算机技术与发展,2008,(08):65.
　CFIEN Hong,LIANG Wen-bin,LI Zong-bao,et al.A Neural Network Predictive Control Algorithm Based on Robot[J].,2008,(07):65.
[7]范莉丽 王奇志.改进的生物激励神经网络的机器人路径规划[J].计算机技术与发展,2006,(04):19.
　FAN Li-li,WANG Qi-zhi.Robot Path Planning of Modified Biologically Inspired Neural Networks[J].,2006,(07):19.
[8]王肖青 王奇志.传统人工势场的改进[J].计算机技术与发展,2006,(04):96.
　WANG Xiao-qing,WANG Qi-zhi.An Evolutionary Method of Traditional Artificial Potential Field[J].,2006,(07):96.
[9]于锐 曹介南 朱培栋.车辆运输路径规划问题研究[J].计算机技术与发展,2011,(01):5.
　YU Rui,CAO Jie-nan,ZHU Pei-dong.Research for Routing Planning of Vehicle Transportation[J].,2011,(07):5.
[10]孙柏林 高珏 孔超[] 许华虎[].一种家居机器人路径规划方法的研究[J].计算机技术与发展,2012,(03):67.
　SUN Bai-lin,GAO Jue,KONG Chao,et al.Study on Path Planning of Indoor Robots[J].,2012,(07):67.
[11]张荣松 包家汉.基于改进遗传算法的机器人路径规划[J].计算机技术与发展,2009,(07):20.
　ZHANG Rong-song,BAO Jia-han.Robot Path Planning Based on Modified Genetic Algorithm[J].,2009,(07):20.
[12]陈得宝 李庆 李群 李峥.基于内分泌思想的改进粒子群算法[J].计算机技术与发展,2008,(10):61.
　CHEN De-bao,LI Qing,LI Qun,et al.An Improved Particle Swarm Algorithm Based on Endocrine Idea[J].,2008,(07):61.
[13]刘钰 陆建峰 蔡海舟.基于改进A＊算法的机器人路径规划方法研究[J].计算机技术与发展,2012,(12):108.
　LIU Yu,LU Jian-feng,CAI Hai-zhou.Research on Path Planning Method of Robot Based on Improved A ＊ Algorithm[J].,2012,(07):108.

更新日期/Last Update: 2019-07-10