# Control of a Mobile Line-Following Robot Using Neural Networks
## 1 Introduction  
In recent years, artificial intelligence techniques, such as machine learning and deep learning, have revolutionized various fields of science and engineering, including robotics [1, 2, 3, 4]. These techniques have shown significant potential to enhance the autonomy and intelligence of robots, enabling them to perform complex tasks with greater efficiency and precision. One of the promising fields where these techniques are currently applied is the control of mobile robots, including Autonomous Guided Vehicles (AGVs).  
Traditionally, techniques such as PID control and trajectory planning algorithms have been used in mobile robot control. However, these approaches face limitations in dynamic and unstructured environments. Deep learning (DL) techniques offer a data-driven approach that can enhance the adaptability and intelligence of these control systems. Inspired by recent advances in artificial intelligence, modern alternatives like LSTM and CNN networks have emerged, offering potential solutions to these challenges.  
In this context, the development of a line-following robot control algorithm using DL techniques was chosen. This practical and relevant application enables one to demonstrate the effectiveness of neural networks in robot control, providing solid validation for new control approaches. By comparing traditional and modern techniques, this work aims to identify the advantages and limitations of DL, contributing to the development of smarter and more efficient robotic systems. On the other hand, the use of DL algorithms such as LSTM and CNN does not inherently require a mathematical model of the robot for its control. The need for a mathematical model depends on the specific application and the roleof neural networks in the control system. In the present case, the LSTM or CNN models are trained to predict control actions directly from sensor data, and the robot can be controlled using only the sensor inputs. This data-driven approach relies on neural networks to learn the relationships between sensor input and control output during training. Thus, LSTM is used to process sequential data, such as time series sensor readings, while CNN is used to handle spatial data such as camera images. The results obtained are very promising, showing that the quality and quantity of the training data used were sufficient to provide satisfactory control of the robot in a real scenario. Therefore, the main contributions of this work can be summarized as follows:  
* Introducing the LSTM and CNN architectures for the control of a mobile robot.
* Providing detailed insights into the implementation of various control algorithms, with a particular focus on the LSTM and CNN networks.
* Comparison of artificial intelligence algorithms with the classical PID controller.
* Evaluating and comparing the performance of different control algorithms using various metrics.  
The article is structured as follows. Section 2 presents the robot architecture and its main components. The control methods are described in Section 3. Section 4 discusses the details of the implementation of the three control strategies used, PID, LSTM, and CNN. The results are provided in Section 5, while Section 6 includes a discussion of the real experiments. Finally, Section 7 draws the main conclusions and outlines the future developments of the presented work.