以循環卡爾曼網路的深度學習輔助吊車之吊物擺盪預測

莊昭陽

doi:10.6342/NTU202002034

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以循環卡爾曼網路的深度學習輔助吊車之吊物擺盪預測

Deep-learning method assisted crane for sway prediction: Recurrent Kalman Network

莊昭陽 , Masters　　Advisor：康仕仲　　

繁體中文 DOI： 10.6342/NTU202002034

深度學習；機器學習；人工智慧；時序預測；電腦視覺；吊物系統； deep learning ； machine learning ； artificial intelligence ； time series prediction ； computer vision ； payload system

Abstract │ Reference (19) │ Altmetrics

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Many researchers study the automatic control strategy of cranes, their research needs to know the physical parameters and derive physical models to achieve the control method. However, the physical parameters of the crane cannot observe in practice, which hinders the application of the crane automation methods insite. On the other hand, experienced crane operators can decide the control strategy by visually observing the movement of the payloads and predicting the future position. If a visual prediction method is automated and applied to the crane, which can solve the limitation of the physical parameters of the crane automation method, and the feasibility of applying it to the construction site can be improved. It is a challenge to predict the future position of payloads only by the vision, because computers cannot understand the abstract meaning of the combination of pixels in images. Therefore, this study employs the Recurrent Kalman Network (RKN), which is adept in the problem of high-interference and high-dimensional state, to predict the future state of the payloads. We use the side view of the pendulum generated by the numerical model to train RKN, which learns the motion pattern of the payloads by observing the continuous pendulum image, then get the model. After that, use the camera to collect image data of the movement of the payloads and predict the future position.

This study compares the ability of Long Short-Term Memory (LSTM) and RKN to predict the future position of payloads with images. The results show that the prediction error of RKN reached 1.63 pixels on average, compared with the 22.0 pixels of the LSTM. In addition to capturing the sequence relationship, RKN can also calculate the correlation and uncertainty between data dimensions. Therefore, RKN can predict the future position of the payloads without physical parameters but only given image data.

Reference ( 19 ) 〈TOP〉

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., ... Ghemawat, S. (2016). TensorFlow: large-scale machine learning on heterogeneous systems. Retrieved from https://www.tensorflow.org/
Abderrahim, M., Gimenez, A., Nombela, A., Garrido, S., Diez, R., Padrón, V. M., Balaguer, C. (2001). The design and development of an automatic construction crane. In Proceedings of 18th International Symposium on Automation and Robotics in Construction, pp. 149-154.
Becker, P., Pandya, H., Gebhardt, G.H., Zhao, C., Taylor, C.J., Neumann, G. (2019). Recurrent Kalman Networks: factorized inference in high-dimensional deep feature spaces. arXiv: 1905.07357.
Chen, Q., Cheng, W., Gao, L., Fottner, J. (2019). A pure neural network controller for double‐pendulum crane anti‐sway control: based on Lyapunov stability theory. Asian Journal of Control.
Chollet, F. (2015). Keras. Retrieved from https://keras.io

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