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Web AR Solution for UAV Pilot Training and Usability Testing
Data and services are available anywhere at any time thanks to the Internet and mobile devices. Nowadays, there are new ways of representing data through trendy technologies such as augmented reality (AR), which extends our perception of reality through the addition of a virtual layer on top of real...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7922183/ https://www.ncbi.nlm.nih.gov/pubmed/33669733 http://dx.doi.org/10.3390/s21041456 |
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author | Ribeiro, Roberto Ramos, João Safadinho, David Reis, Arsénio Rabadão, Carlos Barroso, João Pereira, António |
author_facet | Ribeiro, Roberto Ramos, João Safadinho, David Reis, Arsénio Rabadão, Carlos Barroso, João Pereira, António |
author_sort | Ribeiro, Roberto |
collection | PubMed |
description | Data and services are available anywhere at any time thanks to the Internet and mobile devices. Nowadays, there are new ways of representing data through trendy technologies such as augmented reality (AR), which extends our perception of reality through the addition of a virtual layer on top of real-time images. The great potential of unmanned aerial vehicles (UAVs) for carrying out routine and professional tasks has encouraged their use in the creation of several services, such as package delivery or industrial maintenance. Unfortunately, drone piloting is difficult to learn and requires specific training. Since regular training is performed with virtual simulations, we decided to propose a multiplatform cloud-hosted solution based in Web AR for drone training and usability testing. This solution defines a configurable trajectory through virtual elements represented over barcode markers placed on a real environment. The main goal is to provide an inclusive and accessible training solution which could be used by anyone who wants to learn how to pilot or test research related to UAV control. For this paper, we reviewed drones, AR, and human–drone interaction (HDI) to propose an architecture and implement a prototype, which was built using a Raspberry Pi 3, a camera, and barcode markers. The validation was conducted using several test scenarios. The results show that a real-time AR experience for drone pilot training and usability testing is achievable through web technologies. Some of the advantages of this approach, compared to traditional methods, are its high availability by using the web and other ubiquitous devices; the minimization of technophobia related to crashes; and the development of cost-effective alternatives to train pilots and make the testing phase easier for drone researchers and developers through trendy technologies. |
format | Online Article Text |
id | pubmed-7922183 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79221832021-03-03 Web AR Solution for UAV Pilot Training and Usability Testing Ribeiro, Roberto Ramos, João Safadinho, David Reis, Arsénio Rabadão, Carlos Barroso, João Pereira, António Sensors (Basel) Article Data and services are available anywhere at any time thanks to the Internet and mobile devices. Nowadays, there are new ways of representing data through trendy technologies such as augmented reality (AR), which extends our perception of reality through the addition of a virtual layer on top of real-time images. The great potential of unmanned aerial vehicles (UAVs) for carrying out routine and professional tasks has encouraged their use in the creation of several services, such as package delivery or industrial maintenance. Unfortunately, drone piloting is difficult to learn and requires specific training. Since regular training is performed with virtual simulations, we decided to propose a multiplatform cloud-hosted solution based in Web AR for drone training and usability testing. This solution defines a configurable trajectory through virtual elements represented over barcode markers placed on a real environment. The main goal is to provide an inclusive and accessible training solution which could be used by anyone who wants to learn how to pilot or test research related to UAV control. For this paper, we reviewed drones, AR, and human–drone interaction (HDI) to propose an architecture and implement a prototype, which was built using a Raspberry Pi 3, a camera, and barcode markers. The validation was conducted using several test scenarios. The results show that a real-time AR experience for drone pilot training and usability testing is achievable through web technologies. Some of the advantages of this approach, compared to traditional methods, are its high availability by using the web and other ubiquitous devices; the minimization of technophobia related to crashes; and the development of cost-effective alternatives to train pilots and make the testing phase easier for drone researchers and developers through trendy technologies. MDPI 2021-02-19 /pmc/articles/PMC7922183/ /pubmed/33669733 http://dx.doi.org/10.3390/s21041456 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ribeiro, Roberto Ramos, João Safadinho, David Reis, Arsénio Rabadão, Carlos Barroso, João Pereira, António Web AR Solution for UAV Pilot Training and Usability Testing |
title | Web AR Solution for UAV Pilot Training and Usability Testing |
title_full | Web AR Solution for UAV Pilot Training and Usability Testing |
title_fullStr | Web AR Solution for UAV Pilot Training and Usability Testing |
title_full_unstemmed | Web AR Solution for UAV Pilot Training and Usability Testing |
title_short | Web AR Solution for UAV Pilot Training and Usability Testing |
title_sort | web ar solution for uav pilot training and usability testing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7922183/ https://www.ncbi.nlm.nih.gov/pubmed/33669733 http://dx.doi.org/10.3390/s21041456 |
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