Forecasting Robot Movement with Sensor Readings and Multi-Layer Perceptron Models

Abstract

 Classification of sensor data plays a crucial role in different fields, aiding in important tasks like detecting faults, recognizing events and predicting maintenance needs. This research paper thoroughly explores the use of machine learning methods, in specific Multi-Layer Perceptron (MLP) networks, to classify sensor data. The dataset used in this study includes raw measurements from all 24 ultrasound sensors alongside their corresponding class labels indicating the robots actions (such as moving or turning left). The study tackles challenges like imbalanced classes, noisy signals, and striving for classification through a case study employing MLP models. Through conducting experiments and analysis, we fine-tuned the MLP models setup to achieve a 93.04% accuracy on the test dataset. Additionally evaluation metrics like precision, recall and F1 score highlighted the models effectiveness across different classes. A comparison with Support Vector Machines (SVM) and Logistic Regression models highlighted the performance of the MLP model. These results not only show the effectiveness of MLP networks but also provide valuable insights into best practices, for classifying sensor data. Through examining the intricacies of sensor data analysis and classification this study contributes to enhancing our knowledge of applying machine learning to real world challenges.