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

Abstract

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.