Fault detection in wheeled mobile robot based Machine Learning

Robotics gained in importance the attention of researchers nowadays in many fields, in particular monitoring and control. Deployed in harsh environments, Artificial Intelligence has shown a powerful ability to detect and diagnose faults. In this paper, a classification of defects is evaluated using different machines. learning techniques such as Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Recurrent Neural network (RNN). A comparative analysis is carried out among the techniques previously mentioned on the basis of detection accuracy (DA), true Positive rate (TPR), Matthews correlation coefficients (MCC) and false alarm rate (FAR).