Um ambiente para teste e diagnóstico de drones usando cossimulação
The unmanned aerial vehicles (UAVs), also know as drones, are very important to execute flights with no necessary pilot in the vehicle, thus it is programmed to run flight missions. However, they require reliability to execute missions, then with diagnostic it is possible to predict vehicle failure du...
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| Format: | master thesis |
| Status: | Published version |
| Publication Date: | 2019 |
| Country: | Brasil |
| Institution: | Universidade Federal da Paraíba (UFPB) |
| Repository: | Biblioteca Digital de Teses e Dissertações da UFPB |
| Language: | Portuguese |
| OAI Identifier: | oai:repositorio.ufpb.br:123456789/15214 |
| Online Access: | https://repositorio.ufpb.br/jspui/handle/123456789/15214 |
| Access Level: | Open access |
| Keyword: | Veículo aéreo não tripulado Teste Simulação Hardware-in-the-loop High Level Architecture Unmanned Aerial Vehicle Testing Hardware-in-the-loop Simulation Teste de validade - Drones Drones - Avaliação Teste e diagnóstico Cossimulação CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
| Summary: | The unmanned aerial vehicles (UAVs), also know as drones, are very important to execute flights with no necessary pilot in the vehicle, thus it is programmed to run flight missions. However, they require reliability to execute missions, then with diagnostic it is possible to predict vehicle failure during or before the flight. The objective of this work is to present a testing tool, which analyzes and evaluates drones during the flight in indoor environments. For this purpose, the frameworks Ptolemy II was extended for communication with real drones using the High Level Architecture (HLA). The presented testing environment is extendable for other testing routines, and is ready for integration with other simulation and analysis tools. For testing, a total of 40 flights were performed. From that, 20 were used to train a Decision Tree algorithm, and the other 20 to test the algorithm, where some of them had ananomaly added to one ofthe propellers. The accuraterate of fault detection was 70%. |
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