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.
A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge / Boschi, Anna; Salvetti, Francesco; Mazzia, Vittorio; Chiaberge, Marcello. - In: MACHINES. - ISSN 2075-1702. - ELETTRONICO. - 8:3(2020), pp. 49-67. [10.3390/machines8030049]
|Titolo:||A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge|
|Data di pubblicazione:||2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.3390/machines8030049|
|Appare nelle tipologie:||1.1 Articolo in rivista|