A Machine Learning Platform for Multirotor Activity Training and Recognition

Abstract

Machine learning is a new paradigm of problem solving. Teaching machine learning in schools and colleges to prepare the industry's needs becomes imminent, not only in computing majors, but also in all engineering disciplines. This paper develops a new, hands-on approach to teaching machine learning by training a linear classifier and applying that classifier to solve Multirotor Activity Recognition (MAR) problems in an online lab setting. MAR labs leverage cloud computing and data storage technologies to host a versatile environment capable of logging, orchestrating, and visualizing the solution for an MAR problem through a user interface. This work extends Arizona State University's Visual IoT/Robotics Programming Language Environment (VIPLE) as a control platform for multi-rotors used in data collection. VIPLE is a platform developed for teaching computational thinking, visual programming, Internet of Things (IoT) and robotics application development.