Date of Award

5-2026

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Mechanical Engineering

First Advisor

Ryan R. Striker

Abstract

In the field of autonomous vehicle (AV) research, developers often face a significant gap between low-power hobbyist kits and cost-prohibitive industrial platforms. This thesis presents the design, integration, and validation of "Das Mueller," a high-performance, 1/10th-scale unmanned ground vehicle (UGV) designed to provide a transparent and cost-effective testbed for neural network development. By bridging the interoperability gap between the Donkey Car software framework and the 3D-printable Tarmo5 mechanical chassis, this project establishes a modular architecture centered on the NVIDIA Jetson Orin NX single-board computer.

The research utilizes a Model-Based Systems Engineering (MBSE) approach and rapid prototyping to address critical challenges in hardware integration and operational safety. Key technical contributions include the development of a custom mounting adapter to unify disparate hardware ecosystems and the engineering of a dual-rail power distribution system (3S for drivetrain, 4S for logic) to provide an untethered power supply. To ensure safe teleoperation at high speeds, a 20Hz network heartbeat failsafe was implemented, effectively mitigating the "runaway" risks inherent in wireless control links by reverting the vehicle to a neutral state in case of signal loss.

The system's performance was validated through a rigorous testing protocol covering component-level I2C/GPIO handshakes, PWM calibration, and field testing in various environments. While the initial build cost reached $1,570.30 due to high-tier donor hardware, the study identifies clear pathways to reduce the cost to approximately $712 without sacrificing core functionality. The results demonstrate that the integrated platform offers superior mechanical transparency and durability compared to commercial-off-the-shelf alternatives. By leveraging the high impact resilience and fatigue resistance of 3D-printed components, this work establishes a 'known-good' configuration for researchers and provides a robust hardware foundation for advancing autonomous vehicle algorithms.

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