Tuning of Model Predictive Control for Loading Motion of Automated Excavator Using Deep Reinforcement Learning

Author:

Ishihara Shinji1,Ohtsuka Toshiyuki2

Affiliation:

1. Hitachi Ltd.,Research & Development Group

2. Kyoto University, Graduate School of Informatics, Department of Informatics

Publisher

Institute of Electrical Engineers of Japan (IEE Japan)

Reference19 articles.

1. (1) Y. Shimano, Y. Kami, and K. Shimokaze: “Development of PC210LCi-10/PC200i-10 machine control hydraulic excavator”, Komatsu technical report (2014)

2. (2) ICT Hydraulic Excavator ZX200X-5B: Hitachi Review, Vol. 99, No. 1, 111 (2017)

3. (3) D. A. Bradley and D. W. Seward: “Developing real-time autonomous excavation-the LUCIE story”, In Proceedings of 1995 34th IEEE Conference on Decision and Control, Vol. 3, pp. 3028-3033 (1995)

4. (4) A. Stentz, J. Bares, S. Singh, and P. Rowe: “A robotic excavator for autonomous truck loading”, Autonomous Robots, Vol. 7, No. 2, pp. 175-186 (1999)

5. (5) F. A. Bender, S. Göltz, T. Bräunl, and O. Sawodny: “Modeling and offset-free model predictive control of a hydraulic mini excavator”, IEEE Transactions on Automation Science and Engineering, Vol. 14, No. 4, pp. 1682-1694 (2017)

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