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JRM Vol.28 No.5 pp. 715-721
doi: 10.20965/jrm.2016.p0715
(2016)

Paper:

Feature Extraction for Excavator Operation Skill Using CMAC

Kazushige Koiwai*, Yuntao Liao**, Toru Yamamoto*, Takao Nanjo***, Yoichiro Yamazaki***, and Yoshiaki Fujimoto***

*Institute of Engineering, Hiroshima University
1-4-1 Kagamiyama, Higashi-hiroshima-city, Hiroshima 739-8527, Japan

**Graduate School of Engineering, Hiroshima University
1-4-1 Kagamiyama, Higashi-hiroshima-city, Hiroshima 739-8527, Japan

***Global Engineering Center, Kobelco Construction Machinery Co., Ltd.
2-1 Itsukaichikou 2-chome, Saeki-ku, Hiroshima 731-5161, Japan

Received:
March 20, 2016
Accepted:
June 2, 2016
Published:
October 20, 2016
Keywords:
PID controller, CMAC, human skill, excavator, neural network
Abstract

Feature Extraction for Excavator Operation Skill Using CMAC

Feature extraction for excavator operation

In recent years, technology that includes informatization and automation has been introduced in the construction field. On the other hand, those field still require human operation technology based on experience and skills because various environmental conditions vary from hour to hour. Seasoned technicians have made such operation technology effective at various sites and established skillful techniques. However, the decreasing number and aging of skilled technicians are a social issue, making the skill tradition and development of younger technicians difficult at operation sites that require skillful techniques. This study assumed that the operation of machines by an operator was synonymous with the control of systems by a controller; human operation techniques were considered from the viewpoint of control engineering by regarding an operator as a controller. The control system used to represent the operator consisted of a proportional-integral-derivative (PID) controller and a cerebellar model articulation controller (CMAC) that adjusted the PID gains. A CMAC which is a type of neural network learns human skills as variations in the PID gains and expresses them based on the variations. This study applies the proposed method to a hydraulic excavator swing operation to evaluate skills. Moreover, the difference in the operation skills for the excavator is clarified by obtaining operation data for skilled and younger technicians and examining the variation tendency of PID gains.

Cite this article as:
K. Koiwai, Y. Liao, T. Yamamoto, T. Nanjo, Y. Yamazaki, and Y. Fujimoto, “Feature Extraction for Excavator Operation Skill Using CMAC,” J. Robot. Mechatron., Vol.28, No.5, pp. 715-721, 2016.
Data files:
References
  1. [1] H. Yamada, H. Kato, and T. Muto, “Master-Slave Control for Construction Robot Teleoperation,” J. of Robotics and Mechatronics, Vol.15, No.1, pp. 54-60, 2002.
  2. [2] H. Yamada, G. Ming-de, and Z. Dingxuan, “Master-Slave Control for Construction Robot Teleoperation – Application of a Velocity Control with a Force Feedback Model –” J. of Robotics and Mechatronics, Vol.19, No.1, pp. 60-67, 2006.
  3. [3] H. Yoshinada, K. Okamura, and S. Yokota, “Master-Slave Control Method for Hydraulic Excavator,” J. of Robotics and Mechatronics, Vol.24, No.6, pp. 977-984, 2012.
  4. [4] H. Yamada, K. Takeichi, and T. Mato, “Sliding Mode Control of Hydraulic Power Shovel,” J. of Robotics and Mechatronics, Vol.15, No.1, pp. 47-53, 2003.
  5. [5] T. Kakizaki, “i-Construction,” monthly journal Kensetsu, Vol.60, No.1, pp. 6-9, 2016 (in Japanese).
  6. [6] M. Fujita, M. Kamata, and K. Miyata, “A Study on Skill Extraction in the Machine Operation,” Trans. of JSME, Series C, Vol.66, No.666, pp. 547-554, 2002 (in Japanese).
  7. [7] Y. Kawazoe, K. Enomoto, J. Matsumoto, S. Okabe, and S. Handa, “Acquisition of Human Operator’s Skills and Dexterity using Neural Network: Automatic Generation of Neural Controller from Chaotic Time Series Data during Stabilizing Control of an Inverted Pendulum,” Proc. of 29th SICE Symposium on Intelligent Systems, pp. 217-222, 2002 (in Japanese).
  8. [8] K. Miyata and M. Kamata, “Machine Operation Assistance by Using Predictive Information,” Tran. of JSME, Series C, Vol.72, No.713, pp. 132-138, 2006 (in Japanese).
  9. [9] M. Kato, T. Yamamoto, and S. Fujisawa, “A Skill-Based PID Controller Using Artificial Neural Networks,” Proc. of Int. Conf. on Computational Intelligence for Modeling, Control and Automation, Vienna, pp. 702-705, 2005.
  10. [10] J. S. Albus, “A New Approach to Manipulator Control Cerebellar Articulation Control (CMAC),” Trans. on ASME, J. of Dynamics Systems, Measurement, and Control, Vol.97, No.9, pp. 220-227, 1975.
  11. [11] T. Yamamoto, R. Kurozumi, and S. Fujisawa, “A Design of CMAC Based Intelligent PID Controllers,” Artificial Neural Networks and Neural Information Processing, Lecture Notes in Computer Science, Vol.2714, pp. 471-478, 2003.
  12. [12] K. Koiwai, K. Kawada, and T. Yamamoto, “Design and Experimental Evaluation of an Intelligent PID Controller using CMAC,” Proc. of IEEE Int. Conf. on Networking, Sensing and Control, Okayama, pp. 740-745, 2009.
  13. [13] T. Yamamoto and M. Oshima, “Present, Past and Future in Process Control,” J. of SICE, Vol.42, No.4, pp. 330-334, 2003 (in Japanese).

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Last updated on Nov. 20, 2018