Edge Machine Learning for the Automated Decision and Visual Computing of the Robots, IoT Embedded Devices or UAV-Drones

Toma, Cristian; Popa, Marius; Iancu, Bogdan; Doinea, Mihai; Pascu, Andreea; Ioan-Dutescu, Filip

doi:10.3390/electronics11213507

Open AccessArticle

Edge Machine Learning for the Automated Decision and Visual Computing of the Robots, IoT Embedded Devices or UAV-Drones

by

Cristian Toma

^*

,

Marius Popa

^*

,

Bogdan Iancu

,

Mihai Doinea

^*

,

Andreea Pascu

and

Filip Ioan-Dutescu

Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania

^*

Authors to whom correspondence should be addressed.

Electronics 2022, 11(21), 3507; https://doi.org/10.3390/electronics11213507

Submission received: 28 August 2022 / Revised: 18 October 2022 / Accepted: 26 October 2022 / Published: 28 October 2022

(This article belongs to the Section Artificial Intelligence)

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Abstract

This paper presents edge machine learning (ML) technology and the challenges of its implementation into various proof-of-concept solutions developed by the authors. Paper presents the concept of Edge ML from a variety of perspectives, describing different implementations such as: a tech-glove smart device (IoT embedded device) for controlling teleoperated robots or an UAVs (unmanned aerial vehicles/drones) that is processing data locally (at the device level) using machine learning techniques and artificial intelligence neural networks (deep learning algorithms), to make decisions without interrogating the cloud platforms. Implementation challenges used in Edge ML are described and analyzed in comparisons with other solutions. An IoT embedded device integrated into a tech glove, which controls a teleoperated robot, is used to run the AI neural network inference. The neural network was trained in an ML cloud for better control. Implementation developments, behind the UAV device capable of visual computation using machine learning, are presented.

Keywords: machine learning; deep learning; Edge ML; robots; IoT—Internet of Things; UAV—drones

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MDPI and ACS Style

Toma, C.; Popa, M.; Iancu, B.; Doinea, M.; Pascu, A.; Ioan-Dutescu, F. Edge Machine Learning for the Automated Decision and Visual Computing of the Robots, IoT Embedded Devices or UAV-Drones. Electronics 2022, 11, 3507. https://doi.org/10.3390/electronics11213507

AMA Style

Toma C, Popa M, Iancu B, Doinea M, Pascu A, Ioan-Dutescu F. Edge Machine Learning for the Automated Decision and Visual Computing of the Robots, IoT Embedded Devices or UAV-Drones. Electronics. 2022; 11(21):3507. https://doi.org/10.3390/electronics11213507

Chicago/Turabian Style

Toma, Cristian, Marius Popa, Bogdan Iancu, Mihai Doinea, Andreea Pascu, and Filip Ioan-Dutescu. 2022. "Edge Machine Learning for the Automated Decision and Visual Computing of the Robots, IoT Embedded Devices or UAV-Drones" Electronics 11, no. 21: 3507. https://doi.org/10.3390/electronics11213507

APA Style

Toma, C., Popa, M., Iancu, B., Doinea, M., Pascu, A., & Ioan-Dutescu, F. (2022). Edge Machine Learning for the Automated Decision and Visual Computing of the Robots, IoT Embedded Devices or UAV-Drones. Electronics, 11(21), 3507. https://doi.org/10.3390/electronics11213507

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Edge Machine Learning for the Automated Decision and Visual Computing of the Robots, IoT Embedded Devices or UAV-Drones

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