A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/8/3/49 |
| _version_ | 1827707347486638080 |
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| author | Anna Boschi Francesco Salvetti Vittorio Mazzia Marcello Chiaberge |
| author_facet | Anna Boschi Francesco Salvetti Vittorio Mazzia Marcello Chiaberge |
| author_sort | Anna Boschi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T16:45:25Z |
| format | Article |
| id | doaj.art-f20bba4da57b4c3191b66a5ad358da5d |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T16:45:25Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-f20bba4da57b4c3191b66a5ad358da5d2023-11-20T11:43:09ZengMDPI AGMachines2075-17022020-08-01834910.3390/machines8030049A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the EdgeAnna Boschi0Francesco Salvetti1Vittorio Mazzia2Marcello Chiaberge3Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, ItalyDepartment of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, ItalyDepartment of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, ItalyDepartment of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, ItalyThe 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.https://www.mdpi.com/2075-1702/8/3/49person-followingroboticsdeep learningedge AI |
| spellingShingle | Anna Boschi Francesco Salvetti Vittorio Mazzia Marcello Chiaberge A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge Machines person-following robotics deep learning edge AI |
| title | A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge |
| title_full | A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge |
| title_fullStr | A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge |
| title_full_unstemmed | A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge |
| title_short | A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge |
| title_sort | cost effective person following system for assistive unmanned vehicles with deep learning at the edge |
| topic | person-following robotics deep learning edge AI |
| url | https://www.mdpi.com/2075-1702/8/3/49 |
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