Control of a Mobile Line-Following Robot Using Neural Networks - Hugo M. Leal; Ramiro S. Barbosa; Isabel S. Jesus (Algorithms)
This work aims to develop and compare the performance of a line-following robot using both neural networks and classical controllers such as Proportional–Integral–Derivative (PID). Initially, the robot’s infrared sensors were employed to follow a line using a PID controller. The data from this method were then used to train a Long Short-Term Memory (LSTM) network, which successfully replicated the behavior of the PID controller. In a subsequent experiment, the robot’s camera was used for line-following with neural networks. Images of the track were captured, categorized, and used to train a convolutional neural network (CNN), which then controlled the robot in real time. The results showed that neural networks are effective but require more processing and calibration. On the other hand, PID controllers proved to be simpler and more efficient for the tested tracks. Although neural networks are very promising for advanced applications, they are also capable of handling simpler tasks effectively.