Next Article in Journal
Weight-Vibration Pareto Optimization of a Triple Mass Flywheel for Heavy-Duty Truck Powertrains
Previous Article in Journal
Analysis of a Wearable Robotic System for Ankle Rehabilitation
Previous Article in Special Issue
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
Open AccessArticle

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
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop