UruBots Autonomous Cars Challenge Pro Team Description Paper for FIRA 2025
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909642837196800 |
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| author | Moraes, Pablo Rodríguez, Mónica Barcelona, Sebastian Da Silva, Angel Fernandez, Santiago Sodre, Hiago Nunes, Igor Guterres, Bruna Grando, Ricardo |
| author_facet | Moraes, Pablo Rodríguez, Mónica Barcelona, Sebastian Da Silva, Angel Fernandez, Santiago Sodre, Hiago Nunes, Igor Guterres, Bruna Grando, Ricardo |
| contents | This paper describes the development of an autonomous car by the UruBots team for the 2025 FIRA Autonomous Cars Challenge (Pro). The project involves constructing a compact electric vehicle, approximately the size of an RC car, capable of autonomous navigation through different tracks. The design incorporates mechanical and electronic components and machine learning algorithms that enable the vehicle to make real-time navigation decisions based on visual input from a camera. We use deep learning models to process camera images and control vehicle movements. Using a dataset of over ten thousand images, we trained a Convolutional Neural Network (CNN) to drive the vehicle effectively, through two outputs, steering and throttle. The car completed the track in under 30 seconds, achieving a pace of approximately 0.4 meters per second while avoiding obstacles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07348 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UruBots Autonomous Cars Challenge Pro Team Description Paper for FIRA 2025 Moraes, Pablo Rodríguez, Mónica Barcelona, Sebastian Da Silva, Angel Fernandez, Santiago Sodre, Hiago Nunes, Igor Guterres, Bruna Grando, Ricardo Robotics Systems and Control Image and Video Processing This paper describes the development of an autonomous car by the UruBots team for the 2025 FIRA Autonomous Cars Challenge (Pro). The project involves constructing a compact electric vehicle, approximately the size of an RC car, capable of autonomous navigation through different tracks. The design incorporates mechanical and electronic components and machine learning algorithms that enable the vehicle to make real-time navigation decisions based on visual input from a camera. We use deep learning models to process camera images and control vehicle movements. Using a dataset of over ten thousand images, we trained a Convolutional Neural Network (CNN) to drive the vehicle effectively, through two outputs, steering and throttle. The car completed the track in under 30 seconds, achieving a pace of approximately 0.4 meters per second while avoiding obstacles. |
| title | UruBots Autonomous Cars Challenge Pro Team Description Paper for FIRA 2025 |
| topic | Robotics Systems and Control Image and Video Processing |
| url | https://arxiv.org/abs/2506.07348 |