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 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.