Robotic Tele-operation Performance Analysis via Digital Twin Simulations

Abstract

Tele-operated robots are gaining industrial traction for remote inspection and operation in hazardous environments. While the tele-operated robots may have autonomous loops, they must support closed-loop control commands from human operators. This research demonstration aims to develop a realistic digital twin of the remote inspection environment capturing robots, network transmission, and machine learning models in order to study the complex interactions between these components. Along with this, it provides guidelines for performance tuning over the digital twin in order to optimally use robotic, compute, and network resources. The study of the performance of this digital twin relies on various network parameters, size of data transferred, robot speed and other hyperparameters of machine learning model. This could be used to derive specific values for each of them in order to trade-off deployment accuracy and efficiency.