Journal of South China University of Technology (Natural Science Edition) ›› 2017, Vol. 45 ›› Issue (10): 93-99.doi: 10.3969/j.issn.1000-565X.2017.10.013

• Electronics, Communication & Automation Technology • Previous Articles     Next Articles

Sorting Route Optimization of Parallel Robot Based on Improved Genetic Algorithm

ZHANG Hao-jian SU Ting-ting WU Shao-hong ZHENG Jun WANG Yun-kuan   

  1. Institute of Automation∥University of Chinese Academy of Sciences,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2016-11-23 Revised:2017-02-09 Online:2017-10-25 Published:2017-09-01
  • Contact: 吴少泓(1986-) ，女，博士，副研究员，主要从事机器视觉研究． E-mail:shaohong.wu@ia.ac.cn
  • About author:张好剑(1982-)，男，博士生，主要从事机器人技术与智能控制研究． E-mail:zhanghaojian2014@ia．ac．cn
  • Supported by:
    Supported by the Major Science and Technology in Henan Province(161100210300)

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Abstract: By analyzing the production process of packaging lines and aiming at the multiple-target problem of the Delta parallel robot sorting with dynamically changing positions under complex multi-constraint conditions,a me- thod to optimize the sorting trajectories of the parallel robot is proposed based on an improved genetic algorithm and the thought of beats.By analyzing the sorting strategy,the practical problem is transformed into a similar traveling salesman problem (TSP),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 in every beat are opti- mized by transforming the constraint conditions into a genetic operator combining the fission and merge operations of chromosomes.The test results show that,as compared with the traditional methods,the proposed method can ef- fectively shorten the average sorting stroke,with an average efficiency increase of 14. 76%.

Key words: sorting path, thought of beats, trajectory optimization, improved genetic algorithm, traveling sales- man problem, parallel robot

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