Data-Driven Reinforcement-Learning-Based Automatic Bucket-Filling for Wheel Loaders
| Autor: | Guangzong Gao, Jianfei Huang, Xinchun Cheng, Jinshi Chen, Dewen Kong |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
data-driven model
reinforcement learning Technology Computer science Process (engineering) QH301-705.5 media_common.quotation_subject QC1-999 wheel loaders automatic bucket-filling Adaptability Data-driven Convergence (routing) Reinforcement learning General Materials Science Biology (General) Instrumentation QD1-999 media_common Fluid Flow and Transfer Processes business.industry Process Chemistry and Technology Physics General Engineering Statistical model Control engineering Engineering (General). Civil engineering (General) Automation Computer Science Applications Chemistry TA1-2040 business Transfer of learning |
| Zdroj: | Applied Sciences, Vol 11, Iss 9191, p 9191 (2021) Applied Sciences Volume 11 Issue 19 |
| ISSN: | 2076-3417 |
| Popis: | Automation of bucket-filling is of crucial significance to the fully automated systems for wheel loaders. Most previous works are based on a physical model, which cannot adapt to the changeable and complicated working environment. Thus, in this paper, a data-driven reinforcement-learning (RL)-based approach is proposed to achieve automatic bucket-filling. An automatic bucket-filling algorithm based on Q-learning is developed to enhance the adaptability of the autonomous scooping system. A nonlinear, non-parametric statistical model is also built to approximate the real working environment using the actual data obtained from tests. The statistical model is used for predicting the state of wheel loaders in the bucket-filling process. Then, the proposed algorithm is trained on the prediction model. Finally, the results of the training confirm that the proposed algorithm has good performance in adaptability, convergence, and fuel consumption in the absence of a physical model. The results also demonstrate the transfer learning capability of the proposed approach. The proposed method can be applied to different machine-pile environments. |
| Databáze: | OpenAIRE |
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