CASIA OpenIR  > 智能制造技术与系统研究中心  > 先进制造与自动化
基于改进遗传算法的并联机器人分拣路径优化
张好剑1,2; 苏婷婷1,2; 吴少泓1,2; 郑军1,2; 王云宽1,2
Source Publication华南理工大学学报
2017-10-25
Volume2017Issue:10Pages:93
Abstract
通过分析包装流水线的生产工艺,针对复杂的多约束条件下的Delta并联机器人分拣动态变化的
多目标问题,本文提出了分节拍的基于改进遗传算法的并联机器人分拣拾取路径进行优化。本文经过分析分拣策略把实际问题转换为类似旅行商(TSP)问题,并结合其工艺的特殊性提出一种改进遗传算法;首先引入分节拍的处理思想,通过将约束条件变成一种染色体裂变合并操作结合遗传算子优化,对每个节拍内的分拣路径进行规划。试验结果表明,该方法与传统方法相比能够有效缩短分拣行程,平均提高效率14.76%,为生产线上动态变化的多目标分拣路径规划提供了一种有效的方法
Other Abstract

By analyzing the production precess of packaging lines ,aiming at the multiple-target problem of the Delta parellel robot sorting with dynamically changing positions under complex julti-constraint conditions,a method to optimize the sorting trajectories of the parallel robot if proposed based on an improbed genetic algorithm and the thought of beats.By analyzing the sorting strategy,the practical problem is transform into a similar traveling saleman problem,and an improved genetic algorithm is put forward according to the particularity of the sorting process.Moreover,the thought of beats is introduced, and the sorting trajectories is every beat are optimized by transforming the constraint conditions into a genetic operator combining the fission and merge operations of chromosemes.The test results show that,as compared with the traditional methods,the proposed method can effectively shorten the average sorting stroke,with an average effective increase of 14.76%.

Keyword分节拍 路径规划 改进遗传算法 Tsp 并联机器人
Language中文
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20111
Collection智能制造技术与系统研究中心_先进制造与自动化
Corresponding Author吴少泓
Affiliation1.中国科学院自动化研究所
2.中国科学院大学
First Author Affilication中国科学院自动化研究所
Corresponding Author Affilication中国科学院自动化研究所
Recommended Citation
GB/T 7714
张好剑,苏婷婷,吴少泓,等. 基于改进遗传算法的并联机器人分拣路径优化[J]. 华南理工大学学报,2017,2017(10):93.
APA 张好剑,苏婷婷,吴少泓,郑军,&王云宽.(2017).基于改进遗传算法的并联机器人分拣路径优化.华南理工大学学报,2017(10),93.
MLA 张好剑,et al."基于改进遗传算法的并联机器人分拣路径优化".华南理工大学学报 2017.10(2017):93.
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