TY - JOUR
T1 - BN-LSTM-based energy consumption modeling approach for an industrial robot manipulator
AU - Lin, Hsien I.
AU - Mandal, Raja
AU - Wibowo, Fauzy Satrio
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2024/2
Y1 - 2024/2
N2 - Industrial robots (IRs) are widely used to increase productivity and efficiency in manufacturing industries. Therefore, it is critical to reduce the energy consumption of IRs to maximize their use in polishing, assembly, welding, and handling tasks. This study adopted a data-driven modeling approach using a batch-normalized long short-term memory (BN-LSTM) network to construct a robust energy-consumption prediction model for IRs. The adopted method applies batch normalization (BN) to the input-to-hidden transition to allow faster convergence of the model. We compared the prediction accuracy with that of the 1D-ResNet14 model in a UR (UR3e and UR10e) public database. The adopted model achieved a root mean square (RMS) error of 2.82 W compared with the error of 6.52 W achieved by 1D-ResNet14 model prediction, indicating a performance improvement of 56.74%. We also compared the prediction accuracy over the UR3e dataset using machine learning and deep learning models, such as regression trees, linear regression, ensemble trees, support vector regression, multilayer perceptron, and convolutional neural network-gated recurrent unit. Furthermore, the layers of the well-trained UR3e power model were transferred to the UR10e cobot to construct a rapid power model with 80% reduced UR10e datasets. This transfer learning approach showed an RMS error of 3.67 W, outperforming the 1D-ResNet14 model (RMS error: 4.78 W). Finally, the BN-LSTM model was validated using unseen test datasets from the Yaskawa polishing motion task, with an average prediction accuracy of 99%.
AB - Industrial robots (IRs) are widely used to increase productivity and efficiency in manufacturing industries. Therefore, it is critical to reduce the energy consumption of IRs to maximize their use in polishing, assembly, welding, and handling tasks. This study adopted a data-driven modeling approach using a batch-normalized long short-term memory (BN-LSTM) network to construct a robust energy-consumption prediction model for IRs. The adopted method applies batch normalization (BN) to the input-to-hidden transition to allow faster convergence of the model. We compared the prediction accuracy with that of the 1D-ResNet14 model in a UR (UR3e and UR10e) public database. The adopted model achieved a root mean square (RMS) error of 2.82 W compared with the error of 6.52 W achieved by 1D-ResNet14 model prediction, indicating a performance improvement of 56.74%. We also compared the prediction accuracy over the UR3e dataset using machine learning and deep learning models, such as regression trees, linear regression, ensemble trees, support vector regression, multilayer perceptron, and convolutional neural network-gated recurrent unit. Furthermore, the layers of the well-trained UR3e power model were transferred to the UR10e cobot to construct a rapid power model with 80% reduced UR10e datasets. This transfer learning approach showed an RMS error of 3.67 W, outperforming the 1D-ResNet14 model (RMS error: 4.78 W). Finally, the BN-LSTM model was validated using unseen test datasets from the Yaskawa polishing motion task, with an average prediction accuracy of 99%.
KW - BN-LSTM
KW - Deep learning (DL)
KW - Energy consumption (EC)
KW - Industrial robot (IR)
KW - Machine learning (ML)
UR - http://www.scopus.com/inward/record.url?scp=85168307879&partnerID=8YFLogxK
U2 - 10.1016/j.rcim.2023.102629
DO - 10.1016/j.rcim.2023.102629
M3 - Article
AN - SCOPUS:85168307879
SN - 0736-5845
VL - 85
JO - Robotics and Computer-Integrated Manufacturing
JF - Robotics and Computer-Integrated Manufacturing
M1 - 102629
ER -