VIPLE Extensions in Robotic Simulation, Quadrotor Control Platform, and Machine Learning for Multirotor Activity Recognition

• Other Authors: De La Rosa, Matthew Lee (Author)
• Format: Dissertation
• Language: English
• Published: 2018
• Subjects: Computer science; Cloud Computing; Education; Internet of Things; Machine Learning; Orchestration; VIPLE
• Online Access: http://hdl.handle.net/2286/R.I.51679

abstract: Machine learning tutorials often employ an application and runtime specific solution for a given problem in which users are expected to have a broad understanding of data analysis and software programming. This thesis focuses on designing and implementing a new, hands-on approach to teaching machine learning by streamlining the process of generating Inertial Movement Unit (IMU) data from multirotor flight sessions, training a linear classifier, and applying said classifier to solve Multi-rotor 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. MAR labs 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. As a part of this education platform, this work also develops a 3D simulator capable of simulating the programmable behaviors of a robot within a maze environment and builds a physical quadrotor for use in MAR lab experiments. === Dissertation/Thesis === Masters Thesis Computer Science 2018