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Open AccessArticle

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

Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
PIC4SeR, Politecnico di Torino Interdepartmental Centre for Service Robotics, 10129 Turin, Italy
[email protected], Big Data and Data Science Laboratory, 10129 Turin, Italy
Author to whom correspondence should be addressed.
Machines 2020, 8(3), 49;
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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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.

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