Multi-Neural Network Localisation System with Regression and Classification on Football Autonomous Robots
In environments like the RoboCup Middle Size League (MSL), precise and rapid localisation of robots is crucial for effective autonomous interaction. This study addresses the limitations of conventional localisation approaches—often based on single-camera systems or sensors such as LiDAR (Light Detec...
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| Format: | Article |
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MDPI AG
2025-02-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/6/2/27 |
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| author | Carolina Coelho Lopes António Ribeiro Tiago Ribeiro Gil Lopes A. Fernando Ribeiro |
| author_facet | Carolina Coelho Lopes António Ribeiro Tiago Ribeiro Gil Lopes A. Fernando Ribeiro |
| author_sort | Carolina Coelho Lopes |
| collection | DOAJ |
| description | In environments like the RoboCup Middle Size League (MSL), precise and rapid localisation of robots is crucial for effective autonomous interaction. This study addresses the limitations of conventional localisation approaches—often based on single-camera systems or sensors such as LiDAR (Light Detection and Ranging) and infrared—by developing a robust Artificial Intelligence (AI)-based multi-camera system solution. This method uses multiple neural networks, breaking down the problem while taking advantage of both classification and regression methods. The solution includes a classification neural network to detect field markers, such as line intersections, and two regression neural networks: one for calculating the position of the markers, and another for determining the robot’s position in real-time. It takes advantage of both approaches while maintaining the desired performance, accuracy, and robustness, simplifying the training process and adapting it to different scenarios. Designed specifically to meet MSL robotics’s high-speed demands and precision requirements, the system employs data augmentation techniques to ensure resilience against lighting, angles, and position variations. The results show that this optimised approach improves spatial awareness and accuracy, promising robot football advancements. Beyond MSL applications, this method has the potential for broader real-world uses that require dependable, real-time localisation in dynamic settings. |
| format | Article |
| id | doaj-art-e6b204e009e14f67b3074ffcc7b698cd |
| institution | DOAJ |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-e6b204e009e14f67b3074ffcc7b698cd2025-08-20T03:11:03ZengMDPI AGAI2673-26882025-02-01622710.3390/ai6020027Multi-Neural Network Localisation System with Regression and Classification on Football Autonomous RobotsCarolina Coelho Lopes0António Ribeiro1Tiago Ribeiro2Gil Lopes3A. Fernando Ribeiro4Industrial Electronics Department, University of Minho, 4800-058 Guimarães, PortugalIndustrial Electronics Department, University of Minho, 4800-058 Guimarães, PortugalIndustrial Electronics Department, ALGORITMI Centre, 4800-058 Guimarães, PortugalLIACC & ISEP, Polytechnic Institute of Porto, 4249-015 Porto, PortugalIndustrial Electronics Department, ALGORITMI Centre, 4800-058 Guimarães, PortugalIn environments like the RoboCup Middle Size League (MSL), precise and rapid localisation of robots is crucial for effective autonomous interaction. This study addresses the limitations of conventional localisation approaches—often based on single-camera systems or sensors such as LiDAR (Light Detection and Ranging) and infrared—by developing a robust Artificial Intelligence (AI)-based multi-camera system solution. This method uses multiple neural networks, breaking down the problem while taking advantage of both classification and regression methods. The solution includes a classification neural network to detect field markers, such as line intersections, and two regression neural networks: one for calculating the position of the markers, and another for determining the robot’s position in real-time. It takes advantage of both approaches while maintaining the desired performance, accuracy, and robustness, simplifying the training process and adapting it to different scenarios. Designed specifically to meet MSL robotics’s high-speed demands and precision requirements, the system employs data augmentation techniques to ensure resilience against lighting, angles, and position variations. The results show that this optimised approach improves spatial awareness and accuracy, promising robot football advancements. Beyond MSL applications, this method has the potential for broader real-world uses that require dependable, real-time localisation in dynamic settings.https://www.mdpi.com/2673-2688/6/2/27neural networksclassificationregressionmulti-camera localisationartificial intelligencemarker-based positioning |
| spellingShingle | Carolina Coelho Lopes António Ribeiro Tiago Ribeiro Gil Lopes A. Fernando Ribeiro Multi-Neural Network Localisation System with Regression and Classification on Football Autonomous Robots AI neural networks classification regression multi-camera localisation artificial intelligence marker-based positioning |
| title | Multi-Neural Network Localisation System with Regression and Classification on Football Autonomous Robots |
| title_full | Multi-Neural Network Localisation System with Regression and Classification on Football Autonomous Robots |
| title_fullStr | Multi-Neural Network Localisation System with Regression and Classification on Football Autonomous Robots |
| title_full_unstemmed | Multi-Neural Network Localisation System with Regression and Classification on Football Autonomous Robots |
| title_short | Multi-Neural Network Localisation System with Regression and Classification on Football Autonomous Robots |
| title_sort | multi neural network localisation system with regression and classification on football autonomous robots |
| topic | neural networks classification regression multi-camera localisation artificial intelligence marker-based positioning |
| url | https://www.mdpi.com/2673-2688/6/2/27 |
| work_keys_str_mv | AT carolinacoelholopes multineuralnetworklocalisationsystemwithregressionandclassificationonfootballautonomousrobots AT antonioribeiro multineuralnetworklocalisationsystemwithregressionandclassificationonfootballautonomousrobots AT tiagoribeiro multineuralnetworklocalisationsystemwithregressionandclassificationonfootballautonomousrobots AT gillopes multineuralnetworklocalisationsystemwithregressionandclassificationonfootballautonomousrobots AT afernandoribeiro multineuralnetworklocalisationsystemwithregressionandclassificationonfootballautonomousrobots |