Robotic System Control using Embedded Machine Learning and Speech Recognition

Abstract

The focus of this work is to control a computing-resource-constrained robotics system using embedded machine learning (also known as tiny machine learning). The objective of this effort was to develop a potential lab exercise for students in a robotics class, with the goal of giving them a better understanding of how artificial intelligence applications can be used to control a robotics system. This paper discusses the fundamentals of embedded machine learning and the convolution neural network training process used for this project. The dataset used for this project and its preparation for use with the machine learning model is also discussed. An overview of the technology and data used for this project is provided describing hardware features used to control two motors on a robotic system using speech recognition by keyword spotting. Data validation and future improvements of the project related to the speech-controlled system are also discussed.