Abstract
Autonomous planning robotic contact-rich manipulation has long been a challenging problem. Automatic peeling of glass substrates of LCD flat panel displays is a typical contact-rich manipulation task, which requires extremely high safe handling through the manipulation process. To this end of peeling glass substrates automatically, the system model is established from data and is used for the online planning of the robot motion in this paper. A simulation environment is designed to pretrain the process model with deep learning-based neural network structure to avoid expensive and time-consuming collection of real-time data. Then, an online learning algorithm is introduced to tune the pretrained model according to the real-time data from the peeling process experiments to cover the uncertainties of the real process. Finally, an Online Learning Model Predictive Path Integral (OL-MPPI) algorithm is proposed for the optimal trajectory planning of the robot. The performance of our algorithm was validated through glass substrate peeling tasks of experiments.
Highlights
The glass substrate is the base material of LCD flat panel displays, and its thinning process is necessary for the end product being lighter, thinner and smoother [1]
Separating the glass substrate from the pad is needed after the thinning process, and the process of unloading the glass substrate is usually done by human operators
3 shows our algorithmto framework solve the robot manipulation our algorithm framework solve the to robot manipulation planning plannin problem of automated glass substrate peeling formulated in problem of automated glass substrate peeling formulated in Equation (6)
Summary
The glass substrate is the base material of LCD flat panel displays, and its thinning process is necessary for the end product being lighter, thinner and smoother [1]. Separating the glass substrate from the pad is needed after the thinning process, and the process of unloading the glass substrate is usually done by human operators. They use their fingernails to “peel” the glass substrate off the pad from a corner of the substrate (Figure 1a). This manual process is labor-consuming, apt to glass damage, and expensive. Automating the process of unloading the glass substrate has long been expected, and we presented a scheme (Figure 1b) in [2].
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