基于自抗扰控制的3C视觉导航重载AGV系统设计

过金超; 王普杰; 曹宏; 陈进阳

doi:10.12187/2020.02.012

April 2020

Article Contents

GUO Jinchao, WANG Pujie, CAO Hong and et al. Design of 3C visual navigation heavy load AGV system based on auto disturbance rejection control[J]. Journal of Light Industry, 2020, 35(2): 93-99. doi: 10.12187/2020.02.012

Citation: GUO Jinchao, WANG Pujie, CAO Hong and et al. Design of 3C visual navigation heavy load AGV system based on auto disturbance rejection control[J]. Journal of Light Industry, 2020, 35(2): 93-99. doi: 10.12187/2020.02.012 shu

Design of 3C visual navigation heavy load AGV system based on auto disturbance rejection control

1. School of Electrical Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China;
2. He'nan Senyuan Electric Co., Ltd., Changge 450034, China;
3. Shaanxi Non-Ferrous Tian Hong REC Silicon Material Co., Ltd., Yulin 719200, China

Received Date: 2019-11-14

Abstract

Aiming at the problems of the current visual navigation heavy load AGV system using load color band guidance and scanning code positioning, such as complex path laying and color band susceptible to environmental interference, the 3C visual navigation heavy load AGV system based on auto disturbance rejection control(ADRC) was designed. In this design, three independent high-speed monocular cameras were used to improve the structure of AGV, so as to realize the navigation without color band. Cameras were used to scan the data matrix code information of the ground station, and the scanned image deviation information was transmitted to the controller. Through ADRC real-time adjustment of AGV moving track, it realized the navigation and positioning of heavy load AGV. The results of simulation and practical application showed that the system was stable and flexible, AGV had fast response speed, and it could effectively track the trajectory in real time and improve the navigation accuracy. The absolute value of the maximum navigation error was less than 8 mm, and the absolute value of the maximum offset angle was less than 1°.
- auto disturbance rejection control (ADRC),
- 3C visual navigation,
- heavy load AGV,
- navigation accuracy
References
1. [1]
  BACIK J,DUROVSK YF,BIR0S M,et al.Path-finder-development of automated guided vehicle for hospital logistics[J].IEEE Access,2017,5:26892.
2. [2]
  过金超,赵海洋,蒋正柯,等.双向重载智能自主导航车系统设计[J].轻工学报,2017,32(2):97.
3. [3]
  高瑜,过金超,崔光照.一种改进的多机器人路径规划自适应人工势场法[J].郑州轻工业学院学报(自然科学版),2013,28(6):77.
4. [4]
  肖献强,程亚兵,王家恩.基于惯性和视觉复合导航的AGV研究与设计[J].中国机械工程,2019,30(22):1.
5. [5]
  LEE S Y,YANG H W.Navigation of automated guided vehicle suing magnet spot guidance method[J].Robotics & Computer Integrated Manufacturing,2012,28(3):425.
6. [6]
  LU S,XU C,ZHONG R Y.A RFID-enabled positioning system automated guided vehicle for smart factories[J].Journal of Manufacturing Systems,2017,44:179.
7. [7]
  JUNG K,KIM J.Intelligent autonomous systems[M].Berlin Heidelberg:Springer,2013:807-816.
8. [8]
  OSMAN K,GHOMMAM J,SAAD M.Vision based lane reference detection and tracking control of an automated guided vehicle[C]//IEEE Control Systems Society.2017 25th Mediterranean Conference on Control and Automation.Piscataway:IEEE,2017:595.
9. [9]
  XU Z,HUANG S,DING J.A New positioning method for indoor laser navigation on under determined condition[C]//IEEE Control Systems Society.2016 Sixth International Conference on Instrumentation & Measurement,Computer,Communication and Control.Piseatamay:IEEE,2016:703.
10. [10]
  王琳华.磁导式AGV自动导航车控制系统的设计[D].长沙:长沙理工大学,2013.
11. [11]
  江亚峰,王彬彬,袁明新,等.基于自适应反演滑模的全向AGV运动控制[J].计算机仿真,2019,32(2):348.
12. [12]
  韩京清.自抗扰控制技术——估计补偿不确定因素的控制技术[M].北京:国防工业出版社,2008:197-207.
13. [13]
  罗蕊.基于自抗扰控制的移动机器人轨迹跟踪[D].天津:天津工业大学,2018.
Proportional views

Get Citation

PDF

XML

Article Metrics

Article views(1851) PDF downloads(15) Cited by()

Ralated

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Design of 3C visual navigation heavy load AGV system based on auto disturbance rejection control

Abstract

References

Proportional views

Article Metrics

Ralated

Proportional views

通讯作者: 陈斌, bchen63@163.com