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Article

A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge

1
Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
2
PIC4SeR, Politecnico di Torino Interdepartmental Centre for Service Robotics, 10129 Turin, Italy
3
[email protected], Big Data and Data Science Laboratory, 10129 Turin, Italy
*
Author to whom correspondence should be addressed.
Machines 2020, 8(3), 49; https://doi.org/10.3390/machines8030049
Received: 13 July 2020 / Revised: 20 August 2020 / Accepted: 25 August 2020 / Published: 28 August 2020
The vital statistics of the last century highlight a sharp increment of the average age of the world population with a consequent growth of the number of older people. Service robotics applications have the potentiality to provide systems and tools to support the autonomous and self-sufficient older adults in their houses in everyday life, thereby avoiding the task of monitoring them with third parties. In this context, we propose a cost-effective modular solution to detect and follow a person in an indoor, domestic environment. We exploited the latest advancements in deep learning optimization techniques, and we compared different neural network accelerators to provide a robust and flexible person-following system at the edge. Our proposed cost-effective and power-efficient solution is fully-integrable with pre-existing navigation stacks and creates the foundations for the development of fully-autonomous and self-contained service robotics applications. View Full-Text
Keywords: person-following; robotics; deep learning; edge AI person-following; robotics; deep learning; edge AI
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MDPI and ACS Style

Boschi, A.; Salvetti, F.; Mazzia, V.; Chiaberge, M. A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge. Machines 2020, 8, 49. https://doi.org/10.3390/machines8030049

AMA Style

Boschi A, Salvetti F, Mazzia V, Chiaberge M. A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge. Machines. 2020; 8(3):49. https://doi.org/10.3390/machines8030049

Chicago/Turabian Style

Boschi, Anna, Francesco Salvetti, Vittorio Mazzia, and Marcello Chiaberge. 2020. "A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge" Machines 8, no. 3: 49. https://doi.org/10.3390/machines8030049

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