Artificial Neural Networks as Digital Twins for Whispering Gallery Mode Optical Sensors in Robotics Applications

This paper investigates the use of artificial neural networks (ANNs) as a viable digital twin or alternative to the typical whispering gallery mode (WGM) optical sensors in engineering systems, especially in dynamic environments like robotics. Because of its fragility and limited endurance, the WGM sensor which is based on micro-optical resonators is inappropriate in these kinds of situations. In order to address these issues, the paper suggests an ANN that is specifically designed for the system and makes use of the WGM sensor’s high-quality factor (Q-factor). By extending the applicability and endurance to dynamic contexts and reducing fragility problems, the ANN seeks to give high-resolution measurement. In order to minimize post-processing requirements and maintain system robustness, the study goal is for the ANN to function as a representative predictor of the WGM sensor output. The GUCnoid 1.0 humanoid robot is used in the paper as an example to show how the WGM optical sensors may improve humanoid robot performance for a variety of applications. The results of the experiments demonstrate that the sensitivity, precision, and resolution of ANN outputs and actual WGM shifts are equivalent. As a consequence, current obstacles to the widespread use of high-precision sensing in the robotics industry are removed, and the potential of ANNs as virtual substitutes or the digital twin for genuine WGM sensors in robotics systems is validated. So, this paper can be very beneficial not only to the sensing technologies that are used in robotics, which are subjected to the dynamic environments, but also to the industrial automation and human-machine interface.