Control Method for Robotic Manipulation of Heavy Industrial Cables

Mou, Fangli; Wu, Dan

doi:10.1109/icma52036.2021.9512586

Cited by 1 publication

(7 citation statements)

References 18 publications

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“…Next, we evaluate the estimation performance and generalization ability for different modeling methods: the partial least squares regression (PLSR) model in 24 , an MLP network with 6 layers, a CNN with 6 conv layers, a bi-directional LSTM network with a similar structure to 32 and the RCEN described in section “ Learning-based cable effect modeling ”. All these methods use the best cable representation method for training, that is the RF representation for MLP and bi-LSTM, and the EFM representation for CNN and RCEN.…”

Section: Resultsmentioning

confidence: 99%

“…We now present a general scheme to acquire the robust feature points. Our optimization process uses the local effect model proposed in 24 . First, we execute robotic cable manipulation and collect the measurements, then segment the process data and perform feature points selection using the partial least squares regression (PLSR) algorithm for each segment.…”

Section: Methodsmentioning

confidence: 99%

“…This is the basic idea of our work. In our previous work 24 , we use the features-based MSD method to simulate the cable effect, where we apply the partial least squares regression (PLSR) algorithm to perform feature points selection. However, this method has two main limits: First, the linear MSD model is less accurate and realistic, which makes the obtained model become a local model and the accuracy of estimation can be hardly improved.…”

Section: Preliminariesmentioning

confidence: 99%

“…The material presented here is an improved and extended version of the preliminary study presented in 24 , both regarding the methodology and the quality of the results. In this paper, we mainly focus on three aspects of modeling the cable effect: the accuracy of estimation, the effective representation of the manipulated cable and the time efficiency of computing, which are crucial for accomplishing a robotic cable manipulation task in real applications.…”

Section: Preliminariesmentioning

confidence: 99%

“…1 . A method for heavy cable manipulation was proposed in 24 ; however, this method is rather primary and not practical for real applications.…”

Section: Introductionmentioning

confidence: 99%

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Learning-based cable coupling effect modeling for robotic manipulation of heavy industrial cables

Mou

Wang

2022

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The robotic manipulation of a heavy industrial cable is challenging to model and control because of the high number of degrees of freedom and the rigid-flexible coupling dynamics. In this paper, we report the development of modeling the cable effect and control methodology for robotic cable manipulation. Our cable effect model is based on the 2D convolutional neural network, which is a deep learning-based method uses the effective cable representation method to achieve the accurate, generalizable, and efficient estimation of the cable coupling forces and torques. Practical problems such as the measurement limits and time efficiency are considered in our method for real applications. With these approaches, we are the first to solve the problem of dynamic payload effect caused by heavy industrial cables in experimental cases. The used control methodology combines the active disturbance rejection control framework with the sliding mode control method, which can acquire promising tracking performance. We integrate our cable effect model into the control scheme, and demonstrate it satisfies the high-quality robotic manipulation of heavy cables. The performance of the proposed method is assessed with both a simulated system and real robot system. The results show that our method can estimate the cable coupling effect with over 85% accuracy and accomplish manipulation with a positioning error less than 0.01 mm. This reveals that our method is promising for robotic manipulation of heavy industrial cables and can accomplish the challenging cable insertion task.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

“…1 . A method for heavy cable manipulation was proposed in 24 ; however, this method is rather primary and not practical for real applications.…”

Section: Introductionmentioning

confidence: 99%