Edge Machine Learning for the Automated Decision and Visual Computing of the Robots, IoT Embedded Devices or UAV-Drones
Open accessAbstract
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.
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Machine learning can be used to improve the accuracy of object recognition, object tracking, and obstacle detection in robotics drones.
“In the case of robotics drones, machine learning can be used to improve the accuracy of object recognition, object tracking, and obstacle detection.”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [92]
Drones can be trained to recognize different types of objects, such as vehicles or people, and respond accordingly.
“For example, drones can be trained to recognize different types of objects, such as vehicles or people, and respond accordingly [92] .”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [92]
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Artificial intelligence, machine learning and deep learning in advanced robotics, a review
Soori, M., Arezoo, B., & Dastres, R. (2023) · Cognitive Robotics · doi:10.1016/j.cogr.2023.04.001 · Cited by 1,131 · see citing works
- Authors
- Toma, C., Popa, M., Iancu, B., Doinea, M., Pascu, A., & Ioan-Dutescu, F.
- Affiliations
- Bucharest University of Economic Studies (RO)
- Corresponding author
- Toma, Cristian
- Popa, Marius
- Doinea, Mihai
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2022-10-28
- Volume
- 11
- Issue
- 21
- Pages
- 3507
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4307947566
- Cited by
- 22
- References
- 8
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 1 · 2023: 7
- FWCI
- 1.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Bucharest University of Economic Studies
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- edge machine learning, unmanned aerial vehicles, teleoperated robots, neural network inference, deep learning, visual computing
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