Advertisement

Quad-Partitioning-Based Robotic Arm Guidance Based on Image Data Processing with Single Inexpensive Camera For Precisely Picking Bean Defects in Coffee Industry

  • Chen-Ju Kuo
  • Ding-Chau Wang
  • Pin-Xin Lee
  • Tzu-Ting Chen
  • Gwo-Jiun Horng
  • Tz-Heng Hsu
  • Zhi-Jing Tsai
  • Mao-Yuan PaiEmail author
  • Gen-Ming Guo
  • Yu-Chuan Lin
  • Min-Hsiung Hung
  • Chao-Chun Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)

Abstract

In this paper, we propose a bean defect picking system with the quad-partitioning-based robotic arm guidance method, aimed at automatically and precisely picking bean defects in coffee industry. We assume the adopted inexpensive devices, including a robotic arm, a camera, and an IoT (Internet of Things) device, have only basic functions. For successfully picking the small size of beans as possible, stably moving the arm head to the target bean is the key technique in this topic. To achieve this goal under hardware limits, we design an iterative robotic arm guidance method to move the arm head close to the target with quad-partitioning relationships in the camera’s visual space by using image data processing techniques. The error distance after k iterations of the proposed method is approximately estimated as \(\sqrt{( \frac{d_x}{2^{k+1}} )^2 + ( \frac{d_y}{2^{k+1}} )^2}\), where \(d_x\) and \(d_y\) are the width and the length of the field of view. We conduct a case study to validate the proposed method. Testing results show that the proposed system successfully picks bean defects with our proposed robotic arm guidance method.

Keywords

Spatial data analysis Robotic control Industrial automation Iterative adjustment Fault removal 

References

  1. 1.
    Arboleda, E.R., Fajardo, A.C., Medina, R.P.: An image processing technique for coffee black beans identification. In: 2018 IEEE International Conference on Innovative Research and Development (ICIRD), pp. 1–5, May 2018Google Scholar
  2. 2.
    Huang, B., Li, C., Yin, C., Zhao, X.: Cloud manufacturing service platform for small- and medium-sized enterprises. Int. J. Adv. Manuf. Technol. 65(9), 1261–1272 (2013)CrossRefGoogle Scholar
  3. 3.
    Lee, J., Bagheri, B., Kao, H.A.: A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015)CrossRefGoogle Scholar
  4. 4.
    Lee, J., Kao, H.A., Yang, S.: Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP 16, 3–8 (2014)CrossRefGoogle Scholar
  5. 5.
    Pinto, C., Furukawa, J., Fukai, H., Tamura, S.: Classification of green coffee bean images based on defect types using convolutional neural network (CNN). In: 2017 International Conference on Advanced Informatics, Concepts, Theory, and Applications (ICAICTA), pp. 1–5, August 2017Google Scholar
  6. 6.
    Pulli, K., Baksheev, A., Kornyakov, K., Eruhimov, V.: Real-time computer vision with OpenCV. Commun. ACM 55(6), 61–69 (2012)CrossRefGoogle Scholar
  7. 7.
    Salih, Y., Malik, A.S.: Depth and geometry from a single 2D image using triangulation. In: 2012 IEEE International Conference on Multimedia and Expo Workshops, pp. 511–515, July 2012Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chen-Ju Kuo
    • 1
  • Ding-Chau Wang
    • 2
  • Pin-Xin Lee
    • 2
  • Tzu-Ting Chen
    • 1
  • Gwo-Jiun Horng
    • 3
  • Tz-Heng Hsu
    • 3
  • Zhi-Jing Tsai
    • 2
  • Mao-Yuan Pai
    • 4
    Email author
  • Gen-Ming Guo
    • 2
  • Yu-Chuan Lin
    • 1
  • Min-Hsiung Hung
    • 5
  • Chao-Chun Chen
    • 1
  1. 1.IMIS/CSIENational Cheng Kung UniversityTainanTaiwan
  2. 2.MISSouthern Taiwan University of Science and TechnologyTainanTaiwan
  3. 3.CSIESouthern Taiwan University of Science and TechnologyTainanTaiwan
  4. 4.General Research Service CenterNational Pingtung University of Science and TechnologyNeipuTaiwan
  5. 5.CSIEChinese Culture UniversityTaipeiTaiwan

Personalised recommendations