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

Abstract

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.

References

[1] Ali A R, Badawi H H, Algohary M. Acoustic sensor based on fiber-optic polymeric resonator. Optical Society of America B, 2019, 36 509-516.

[2] Geints Y E, Minin I V, Minin O V Concept of miniature optical pressure sensor based on coupled WGMs in a dielectric microsphere, 2021

[3] Ali A R. Micro-optical vibrometer/accelerometer using dielectric microspheres. Applied Optics, 2019, 58(16): 4211-4219.

[4] Ali A R, Saleh A, Abdallah E A, Ali A A, Moubarak F, Elzeky H . Wearable health monitoring and tracking through whispering gallery mode optical sensors in IoT-enabled smartwatches. 2023 International Telecommunications Conference, 2023 337-342

[5] Geints Y E, Minin O V, Minin I V. Proof-of-concept of a miniature pressure sensor based on coupled optical WGMs excited in a dielectric microsphere. Optics & Laser Technology, 2022, 151 108015.

[6] Ali A R, Gatherer A, Ibrahim M S. Spinning optical resonator sensor for torsional vibrational applications measurements. Laser Resonators, Microresonators, and Beam Control XVIII, 2016, 9727 149-155

[7] Ali A R, Afifi A N, Taha H. Optical signal processing and tracking of whispering gallery modes in real-time for sensing applications. Integrated Photonics: Materials, Devices, and Applications IV, 2017, 10249 27-35

[8] Tu Z, Gao D, Zhang M, Zhang D. High-sensitivity complex refractive index sensing based on Fano resonance in the subwavelength grating waveguide micro-ring resonator. Optics Express, 2017, 25(17): 20911-20922.

[9] El Shamy R S, Swillam M A, Li X. Microring resonator for complex refractive index detection at multiple wavelengths. Frontiers in Optics+Laser Science, 2022 JTu4A-49

[10] Halendy M, Ertman S. Whispering-gallery mode micro-ring resonator integrated with a single-core fiber tip for refractive index sensing. Sensors, 2023, 23(23): 9424.

[11] Sahraeibelverdi T, Guo L J, Veladi H, Malekshahi M R. Polymer ring resonator with a partially tapered waveguide for biomedical sensing: computational study. Sensors, 2021, 21(15): 5017.

[12] Butt M A, Kazanskiy N L, Khonina S N. Advances in waveguide Bragg grating structures, platforms, and applications: an up-to-date appraisal. Biosensors, 2022, 12(7): 497.

[13] Butt M A. Integrated optics: platforms and fabrication methods. Encyclopedia, 2023, 3(3): 824-838.

[14] Ali A R, Elias C M. Ultra-sensitive optical resonator for organic solvents detection based on whispering gallery modes. Chemosensors, 2017, 5(2): 19.

[15] Foreman M R. Whispers of absorption. Nature Photonics, 2016, 10(12): 755-757.

[16] Ali A R, Saleh A. High-resolution magnetic field biosensor based on optical resonators. Biophotonics: Photonic Solutions for Better Health Care VI, 2018, 10685 188-199

[17] Ali A R, Algohary M, Wael M, Magdy J. E-nose for odor detection of humanoid robots based on micro opto-mechatronics sensors. 2023 International Microwave and Antenna Symposium, 2023 191-194.

[18] Ali A R, Algouhary M. Novel sensors of the linear displacement measurements with high-resolution for mechatronics applications. Journal of Al-Azhar University Engineering Sector, 2022, 17(62): 275-289.

[19] Stanchieri G D P, Saleh M, Marcellis A D, Ibrahim A, Faccio M, Valle M . FPGA-based tactile sensory platform with optical fiber data link for feedback systems in prosthetics. Electronics, 2023, 12(3): 627.

[20] Ali A R, Ioppolo T. Effect of angular velocity on sensors based on morphology dependent resonances. Sensors, 2014, 14(4): 7041-7048.

[21] Lee R H, Mulder E A B, Hopkins J B. Mechanical neural networks: architected materials that learn behaviors. Science Robotics, 2022, 7(71): eabq7278.

[22] Berger T, Tischler M B, Hagerott S G, Cotting M C, Gray W R. Identification of a full-envelope Learjet-25 simulation model using a stitching architecture. Journal of Guidance, Control, and Dynamics, 2020, 43(11): 2091-2111.

[23] Bernstein L, Sludds A, Panuski C, Trajtenberg-Mills S, Hamerly R, Englund D. Single-shot optical neural network. Science Advances, 2023, 9(25): eadg7904.

[24] Hush M R. Machine learning for quantum physics. Science, 2017, 355(6325): 580-580.

[25] Kavungal D, Magalhães P, Kumar S T, Kolla R, Lashuel H A, Altug H. Artificial intelligence-coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases. Science Advances, 2023, 9(28): eadg9644.

[26] Topal E J. As artificial intelligence goes multimodal, medical applications multiply. Science, 2023, 381(6663): eadk6139.

[27] Anghel D C, Ene A, Belu N. A Matlab neural network application for the study of working conditions. Advanced Materials Research, 2014, 837 310-315.

[28] Al Shamisi M H, Assi AH, Hejase H A N. Using MATLAB to develop artificial neural network models for predicting global solar radiation in Al Ain City - UAE. Engineering Education and Research Using MATLAB, 2011 London, United Kingdom IntechOpen 219-238

[29] Taylor B J, Darrah M A, Moats C D. Verification and validation of neural networks: a sampling of research in progress. Artificial Neural Networks: Methods and Applications, 2003, 5103 8-16

[30] Raissi M, Perdikaris M, Karniadakis G E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Computational Physics, 2019, 378 686-707.

[31] Deng W, Smirnov E, Timoshenko D, Andrianov S. Comparison of regularization methods for ImageNet classification with deep convolutional neural networks. Aasri Procedia, 2014, 6 89-94.

[32] Dreyfus S E. Artificial neural networks, back propagation, and the Kelley-Bryson gradient procedure. Journal of Guidance, Control, and Dynamics, 1990, 13(5): 926-928.

[33] MacKay D J. Bayesian interpolation. Neural Computation, 1992, 4(3): 415-447.

[34] Gill P E, Murray W. Algorithms for the solution of the nonlinear least-squares problem. SIAM Journal on Numerical Analysis, 1978, 15(5): 977-992.

[35] Foresee F D, Hagan M T. Gauss-Newton approximation to Bayesian learning. International Joint on Neural Networks, 1997, 3 1930-1935

[36] Kayri M. Predictive abilities of Bayesian regularization and Levenberg-Marquardt algorithms in artificial neural networks: a comparative empirical study on social data. Mathematical and Computational Applications, 2016, 21(2): 20.

[37] Burden F, Winkler D Livingstone D J. Bayesian regularization of neural networks. Artificial Neural Networks: Methods in Molecular Biology, 2008 Totowa, NJ, USA Humana Press 23-42.

[38] Sariev E, Germano G. Bayesian regularized artificial neural networks for the estimation of the probability of default. Quantitative Finance, 2020, 20(2): 311-328.

[39] Mitchell T M. Artificial neural networks. Machine Learning, 1997, 45(81): 127

[40] Ali A R, Ramadan M W A, Darwish A T. Neural network-based classification of hand gestures using electromyography sensor data. 2023 International Telecommunications Conference, 2023 231-236

[41] Nwankpa C E, Ijomah W, Gachagan A, Marshall S Activation functions: comparison of trends in practice and research for deep learning, 2021

[42] Arjun K S, Aneesh K. Modelling studies by application of artificial neural network using Matlab. Journal of Engineering Science and Technology, 2015, 10(11): 1477-1486

[43] Hagan M T, Demuth H B, Beale M H Neural Network Design, 1997 Boston PWS Publishing Co.

RIGHTS & PERMISSIONS

The Author(s)