Tuning of Model Predictive Control for Loading Motion of Automated Excavator Using Deep Reinforcement Learning
Author:
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)
Link
https://www.jstage.jst.go.jp/article/ieejeiss/144/6/144_552/_pdf
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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