# Control of a Mobile Line-Following Robot Using Neural Networks
## 1 Introduction
#### Literature Review  
In recent years, the use of artificial intelligence and deep learning for autonomous vehicle control has attracted a great deal of interest. In [5], the authors developed a Bluetooth- and vision-controlled AGV for COVID-19, capable of carrying 10 kg and following a labeled user at an optimal distance of 80 cm and a 55\({}^{\circ}\) detection angle. The AGV reduces contact risk, aids seniors by eliminating the need to push heavy loads, and operates via a mobile app or autonomous tracking within a 10 m range. The work by [6] introduced an AI-driven AGV for factory automation, using magnetic tape for navigation to handle goods. The limitations include misjudgment when tape breaks and an inability to change routes around obstacles. The study explored divergence-wheeled robots for smoother navigation in crowded environments. In [7], an AGV was presented for seamless indoor and outdoor navigation, utilizing a self-evolving free space detection (FSD) framework with online active machine learning and sensor data fusion. The framework outperforms DeepLabV3+ by self-learning from real-time multimodal data, reducing the need for large datasets while improving robustness in unstructured environments. In [8], an obstacle avoidance system was implemented for AGVs using deep action learning (DAL) to enhance visual navigation, object recognition, and decision-making. By integrating YOLOv4, SURF, and kNN, the system achieves real-time obstacle avoidance and navigation, meeting Industry 4.0 standards for speed, accuracy, and robustness in unstructured environments. In [9], the authors proposed an AGV obstacle avoidance system combining deep learning object detection and grid-based path planning for dynamic industrial environments. Tested in a simulated tobacco production workshop, the system achieved a 98.67% success rate in obstacle avoidance and improved task efficiency by 27.29%, offering a reliable solution for industrial AGVs. In [10], a deep-reinforcement-learning-based method for automatic PID adjustment to ensure smooth AGV movement was presented. Using the Deep Q-Learning Network (DQN), the approach transforms PID tuning into an action-value optimization problem. Simulations and tests demonstrate the method's effectiveness in achieving optimal PID settings without manual intervention. Another study [11] introduced a two-wheelAGV platform utilizing sensor fusion and neural networks for intelligent path planning and navigation in dynamic unknown environments. The neural network processes sensor inputs to determine safe paths while avoiding arbitrarily shaped and moving obstacles, with simulations validating the approach for Industry 4.0 applications. The work in [12] reviewed recent applications of deep learning in UAVs for tasks like security, disaster rescue, and warehouse management, highlighting the key techniques, their performance, and limitations while discussing the challenges and future directions for UAV-based deep learning solutions. The work in [13] presented an AI algorithm for platform autonomy, enabling navigation without human intervention. Using cameras, LIDAR sensors, and a vision system, the platform employs convolutional neural networks for environment analysis and path optimization. Transfer learning trains the network, with the results demonstrating its effectiveness. In [14], a deep reinforcement learning approach for mapless AGV navigation using LiDAR and RGB cameras was presented. The NavACL-Q method combines curriculum learning with soft actor-critic to address challenges like sparse samples and partial observability. The results demonstrated a 40% performance gain over random starts and a 60% boost with a pre-trained feature extractor, outperforming map-based navigation.