A CNN Pilot for Autonomous Drone Racing
Convolutional neural networks (CNN) and deep learning (DL) have become a popular tool for addressing all kinds of artificial intelligence challenges. The Autonomous Drone Race is a challenge that consists of developing a drone capable of defeating a human in a drone race. DL is a tool that has been...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2020 |
| País: | México |
| Institución: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repositorio: | Repositorio Institucional del INAOE |
| Idioma: | inglés |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/2162 |
| Acceso en línea: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2162 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/Inspec/Autonomous drone racing info:eu-repo/classification/Inspec/CNN y Deep learning info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 info:eu-repo/classification/cti/330412 |
| Sumario: | Convolutional neural networks (CNN) and deep learning (DL) have become a popular tool for addressing all kinds of artificial intelligence challenges. The Autonomous Drone Race is a challenge that consists of developing a drone capable of defeating a human in a drone race. DL is a tool that has been included in state-of-the-art solutions to address this problem. Current work has proposed using CNN and DL to detect the gates, while other work has proposed using a CNN to obtain the flight speed and a three-dimensional reference point, these data are used by the controller to generate the corresponding control signals. It should be noted that all these approaches use a single frame as input. Motivated by the above, this work aims to develop a CNN to obtain the control signals directly for a drone to navigate autonomously in a drone racing circuit. This implies two levels of difficulty: 1) navigating through a predefined sequence of gates; 2) navigating in an environment where the location of the gates is not known a priory. The performance tests were carried out in the Gazebo simulator, using the AR drone vehicle. |
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