Enhancing Accessibility and Affordability of Mobile Robotic Platforms Through MMATR: A Modular Machine-Learning Autonomous Trainable Rover

Traditional mobile robotic platforms often prove cost-prohibitive for small enterprises, offering limited functionality constrained to pre-programmed tasks. The Modular Machine-Learning Autonomous Trainable Rover (MMATR) ad-dresses this gap by striving to democratize access to automated mobile robotics. Prioritizing cost-effectiveness, programma-bility, and user-friendliness, MMATR presents an accessible solution. This research outlines MMATR's primary goals: autonomous operation through embedded machine learning, user-friendly retraining facilitated by a Python GUI, extensi-bility via user-developed modules, and a generatively designed, consumer-sized 3D-printable chassis. MMATR integrates a diverse range of functionalities, including five ultrasonic sensors for autonomous navigation, a user-friendly Python GUI for retraining the embedded machine learning model, and the capa-bility to extend functionality through user-developed modules. This paper details the design, implementation, and potential applications of MMATR, contributing to the advancement of accessible and affordable mobile robotic platforms.