Optical camera communications for platooning applications
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/36684 |
Resumo: | Platooning is a technology that corresponds to all the coordinated movements of a collection of vehicles, or, in the case of mobile robotics, to all the coordinated movements of a collection of mobile robots. It brings several advantages to driving, such as, improved safety, accurate speed control, lower CO2 emission rates, and higher energy efficiency. This dissertation describes the development of a laboratory scale demonstrator of platooning based on optical camera communications, using two generic wheel steered robots. For this purpose, one of the robots is equipped with a Light Emitting Diode (LED) matrix and the other with a camera. The LED matrix acts as an Optical Camera Communication (OCC) transmitter, providing status information of the robot attitude. The camera acts as both image acquisition and as an OCC receiver. The gathered information is processed using the algorithm You Only Look Once (YOLO) to infer the robot motion. The YOLO object detector continuously checks the movement of the robot in front. Performance evaluation of 5 different YOLO models (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv4-tiny-3l) was conducted to assess which model works best for this project. The outcomes demonstrate that YOLOv4-tiny surpasses the other models in terms of timing, making it the ideal choice for real-time performance. Object detection using YOLOv4-tiny was performed on the computer. This was chosen since it has a processing speed of 3.09 fps as opposed to the Raspberry Pi’s 0.2 fps. |
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Optical camera communications for platooning applicationsPlatooningOCCYOLOCameraObject detectionPlatooning is a technology that corresponds to all the coordinated movements of a collection of vehicles, or, in the case of mobile robotics, to all the coordinated movements of a collection of mobile robots. It brings several advantages to driving, such as, improved safety, accurate speed control, lower CO2 emission rates, and higher energy efficiency. This dissertation describes the development of a laboratory scale demonstrator of platooning based on optical camera communications, using two generic wheel steered robots. For this purpose, one of the robots is equipped with a Light Emitting Diode (LED) matrix and the other with a camera. The LED matrix acts as an Optical Camera Communication (OCC) transmitter, providing status information of the robot attitude. The camera acts as both image acquisition and as an OCC receiver. The gathered information is processed using the algorithm You Only Look Once (YOLO) to infer the robot motion. The YOLO object detector continuously checks the movement of the robot in front. Performance evaluation of 5 different YOLO models (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv4-tiny-3l) was conducted to assess which model works best for this project. The outcomes demonstrate that YOLOv4-tiny surpasses the other models in terms of timing, making it the ideal choice for real-time performance. Object detection using YOLOv4-tiny was performed on the computer. This was chosen since it has a processing speed of 3.09 fps as opposed to the Raspberry Pi’s 0.2 fps.O platooning é uma tecnologia que corresponde a todas as movimentações coordenadas de um conjunto de veículos, ou, no caso da robótica movel, a todas as movimentações coordenadas de um conjunto de robots móveis. Traz várias vantagens para a condução, tais como, maior segurança, um controlo preciso da velocidade, menores taxas de emissão de CO2 e maior eficiência energética. Esta dissertação descreve o desenvolvimento de um demonstrador de platooning em escala laboratorial baseado em comunicações com câmera, usando dois robôs móveis genéricos. Para este propósito, um dos robôs é equipado com uma matriz de Light Emitting Diodes (LEDs) e o outro é equipado com uma câmera. A matriz de LEDs funciona como transmissor, fornecendo informações de estado do robô. A câmera funciona como recetor, realizando a aquisição de imagens. As informações recolhidas são processadas usando o algoritmo You Only Look Once (YOLO) de forma a prever o movimento do robô. O YOLO verifica continuamente o movimento do robô da frente. A avaliação de desempenho de 5 modelos de YOLO diferentes (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv4-tiny-3l) foi realizada para identificar qual o modelo que funciona melhor no contexto deste projeto. Os resultados demonstram que o YOLOv4-tiny supera os outros modelos em termos de tempo, tornando-o a escolha ideal para desempenho em tempo real. A deteção de objetos usando YOLOv4-tiny foi realizada no computador. Esta escolhe deveuse ao facto de o computador ter uma velocidade de processamento de 3,09 fps em oposição aos 0,2 fps da Raspberry Pi.2023-03-28T12:47:41Z2022-12-07T00:00:00Z2022-12-07info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/36684engSilva, Beatriz Diasinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T12:10:40Zoai:ria.ua.pt:10773/36684Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:07:23.115486Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Optical camera communications for platooning applications |
title |
Optical camera communications for platooning applications |
spellingShingle |
Optical camera communications for platooning applications Silva, Beatriz Dias Platooning OCC YOLO Camera Object detection |
title_short |
Optical camera communications for platooning applications |
title_full |
Optical camera communications for platooning applications |
title_fullStr |
Optical camera communications for platooning applications |
title_full_unstemmed |
Optical camera communications for platooning applications |
title_sort |
Optical camera communications for platooning applications |
author |
Silva, Beatriz Dias |
author_facet |
Silva, Beatriz Dias |
author_role |
author |
dc.contributor.author.fl_str_mv |
Silva, Beatriz Dias |
dc.subject.por.fl_str_mv |
Platooning OCC YOLO Camera Object detection |
topic |
Platooning OCC YOLO Camera Object detection |
description |
Platooning is a technology that corresponds to all the coordinated movements of a collection of vehicles, or, in the case of mobile robotics, to all the coordinated movements of a collection of mobile robots. It brings several advantages to driving, such as, improved safety, accurate speed control, lower CO2 emission rates, and higher energy efficiency. This dissertation describes the development of a laboratory scale demonstrator of platooning based on optical camera communications, using two generic wheel steered robots. For this purpose, one of the robots is equipped with a Light Emitting Diode (LED) matrix and the other with a camera. The LED matrix acts as an Optical Camera Communication (OCC) transmitter, providing status information of the robot attitude. The camera acts as both image acquisition and as an OCC receiver. The gathered information is processed using the algorithm You Only Look Once (YOLO) to infer the robot motion. The YOLO object detector continuously checks the movement of the robot in front. Performance evaluation of 5 different YOLO models (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv4-tiny-3l) was conducted to assess which model works best for this project. The outcomes demonstrate that YOLOv4-tiny surpasses the other models in terms of timing, making it the ideal choice for real-time performance. Object detection using YOLOv4-tiny was performed on the computer. This was chosen since it has a processing speed of 3.09 fps as opposed to the Raspberry Pi’s 0.2 fps. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12-07T00:00:00Z 2022-12-07 2023-03-28T12:47:41Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10773/36684 |
url |
http://hdl.handle.net/10773/36684 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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