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Machine Learning Based Wireless Interference Estimation in a Robotic Force-Seeking Application

Published

Author(s)

Rick Candell, Mohamed Hany, Karl Montgomery, Yongkang Liu, sebti foufou

Abstract

Wide deployment of wireless communications plays an essential role in the vision of future cyber-physical systems (CPSs), which includes massive transfer of automation information. Many practical considerations of industrial CPSs affect the success of the deployment of industrial wireless networks, and, thus, can inhibit their widespread adoption. These considerations include multi-path propagation, network congestion, and jamming interference. Jamming is of chief concern when wireless is used for mission critical or safety integrated systems. In this paper, an experimental platform consisting of a robot arm depressing a spring mechanism with a wireless force-feedback control algorithm is constructed. The robot applies downward pressure on a spring assembly until a predetermined force is detected and transmitted successfully to the controller under varying levels of sustained interference. Machine learning is used to learn and predict the signal-to-interference level of the communication link solely using position information from an independent vision tracking system. Various supervised learning algorithms are investigated and rated according to their performance.

Citation
NIST Interagency/Internal Report (NISTIR) - 8416
Report Number
8416

Keywords

industrial wireless, factory communications, cyber-physical systems, wireless networking, robotics, machine learning, 802.11

Citation

Candell, R. , Hany, M. , Montgomery, K. , Liu, Y. and Foufou, S. (2022), Machine Learning Based Wireless Interference Estimation in a Robotic Force-Seeking Application, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.IR.8416, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=929248 (Accessed December 4, 2022)
Created February 16, 2022, Updated March 1, 2022