E2.01 RadDAWGs
Sponsor: NASA – Texas Space Grant Consortium (TSGC)
Student Team: Brian Lee, Hunter McCrea, Samuel Drell, Stuart Plaugher
Faculty Advisor: Mr. Jeffrey Stevens
Our project is to design a Radiation-Tolerant Crew Laptop for astronauts to utilize in space. We’re designing a controller to connect a radiation-tolerant processor, the High-Performance Spaceflight Computing processor from MicroChip, to the open-source Framework Laptop. The controller expands hardware options by connecting multiple USB ports to the radiation-tolerant processor enabling the interfacing of the Framework laptop with the processor. Laptops have and will continue to be used on NASA’s new lunar space station — Gateway and our design will enable the support of operational tasks, scientific research, and real-time decision-making in space, thereby reducing downtime and increasing overall mission efficiency.
E2.02 Integrated Camera & Lighting System (ICLS-3)
Sponsor: NASA - Texas Space Grant Consortium (TSGC)
Student Team: Mason Smith, Skylar Jamar, Nicholas Humphrey, Zach Gayman
Faculty Advisor: Dr. Marcelo Carvalho
Our product is a 3D terrain sensing system utilizing mmWave sensors to collect geometric data ahead of a Lunar Terrain Vehicle. This terrain data is quantified and organized with the goal of improving automation for the existing ICLS. Detection and mapping algorithms identify the presence of hazards and map the lunar environment ahead of the vehicle. The terrain data and hazard information is rendered to our display where the operator can manipulate sensor and display parameters.
E2.03 NXP Shield
Sponsor: Ingram School of Engineering (ISoE)
Student Team: Ramon Elizondo, Ian Boyd, Henry Celidon, Carlo Marroquin
Faculty Advisor: Mr. Jeffrey Stevens
Our team is designing a custom peripheral shield for the NXP FRDM-MCXN947 to replace the Blackboard used in Microprocessors labs and cut down on setup time for students. The board will consolidate the core lab hardware (switches, LEDs, pushbuttons, and key peripherals) and route I3C devices through mikroBus click connectors while keeping J1-J4/FlexIO available for direct GPIO work. We are refining the PCB layout to keep headers accessible and the lab process straightforward. We will deliver a fully tested PCB, pin-mapping documentation, and starter lab examples.
E2.04 NXP Peripheral Integration
Sponsor: NXP
Student Team: Andrew Muegel, Alexy Larios, Matthew Coffelt, Stephen Lorenzen
Faculty Advisor: Mr. Mark Welker
This project integrates a custom Peripheral Shield with the NXP FRDM-MCXN947 processor board to replace the Blackboard for EE3320 labs. Our goal is to develop and verify code necessary for demonstrating the functionality of an Arduino compatible shield created by the NXP Peripheral Shield team, featuring motor control, display output, and user interfaces. We will achieve this by programming and testing each subsystem using the NXP MCUXpresso IDE.
E2.05 Mazed & Confused
Sponsor: NXP
Student Team: RJ Salas, Alex Livingston, Ashlee Martinez, Michael Luan
Faculty Advisor: Mr. Mark Welker
Our project is an autonomous robot that uses object detection to travel through a maze efficiently, without contacting the maze walls. It combines sensing, mapping, and movement to navigate the maze. Using a camera and sensors, the system identifies walls, directional indicators, and the designated finish position. Real-time analysis ensures the optimal route is taken, while dual drive motors provide precise turns and speed control, allowing consistent performance under various maze constraints. The skills developed from this project translate directly to modern applications in robotics, automation, and self-driving systems.
E2.06 WayFinder
Sponsor: NXP
Student Team: Shivani Mruthyunjaya, David Tavarez, Darryl Murray, Eignar Cienfuegos
Faculty Advisor: Mr. Mark Welker
Our project is an autonomous robot designed to navigate a maze by using computer vision to interpret external visual indicators and sensors to detect walls for collision avoidance. The robot will begin at a predetermined starting position and autonomously traverse through the maze while making decisions at junctions when multiple movement directions are available. Using a machine learning image classification algorithm, the robot will recognize and classify maze images as left, right, or forward indicators and follow them until it reaches a designated finish image. During the initial exploration run, the robot will record and map the route, allowing it to complete a second traversal more efficiently using the generated map. This project is important due to its potential applications in exploring inaccessible environments and assisting with automated transportation of goods.
E2.07 HDL FPGA 404: BOT_Not_Found
Sponsor: Ingram School of Engineering (ISoE)
Student Team: Theodore Freij, Aaron Ong, Matthew Carter, Ma Sweeney, Alex Nuez, Miguel Octavo Castro, Farrah Balbuena, Luis Leon Pineda
Faculty Advisor: Mr. Mark Welker
Our team is designing an educational development board tailored for Texas State University’s HDL course to enhance hands-on learning in digital systems. We will implement and simulate IIC, SPI, and PWM interfaces on the CORA Z7 FPGA using Xilinx tools like Vivado and Vitis. The final deliverable will be a functioning prototype that supports both physical and digital simulations. This project aims to improve student retention and real-world readiness by integrating practical hardware experience into the curriculum.
E2.08 PLC Elevator
Sponsor: Ingram School of Engineering (ISoE)
Student Team: Kaitlyn Chandler, Nathan Charles, Noah Pham, Sarah Smith
Faculty Advisor: Mr. Jeffrey Stevens
Our project is an automated elevator control system designed using the OpenPLC Editor to ensure safe and efficient passenger transport. The controller manages car and door motor operations, prioritizes and responds to floor requests from both car and hall call buttons, and integrates safety and emergency features for reliable operation. Visual indicators provide real-time status of the elevator car and floor levels, enhancing passenger awareness and system dependability. By combining intelligent control with safety monitoring, our project ensures a secure and effective elevator experience.
E2.09 PLC Elevator
Sponsor: Ingram School of Engineering (ISoE)
Student Team: Justyce Vann, Bryan Jaimes Vallejo, Tianqi Sun, Austin Torres
Faculty Advisor: Mr. Jeffrey Stevens
Our PLC Elevator Control System will demonstrate floor selection, elevator car movement, door operation, safety/ emergency functions, and visual status indicators. The elevator functionality will be communicated through OpenPLC into an Arduino Mega using PLC Ladder Logic. Our Elevator will demonstrate real-life features but just in a miniature model.
E2.10 Torque Titans
Sponsor: Ingram School of Engineering (ISoE)
Student Team: Bryan Arriaga, Gregory Neal Jr., James Walton-Spence
Faculty Advisor: Dr. Lawrence Larson
The goal of this project is to design and build an autonomous sumo robot capable of competing in both sumo style combat and tug of war challenges. The robot must operate entirely on its own using onboard sensing, control logic, and mechanical design optimized for quick detection, strategic movement, and high torque pushing power. All design decisions must fit within strict constraints, including a 13 cm × 13 cm footprint, a regulated weight limit, and a total budget of $75. This project demonstrates how effective engineering, resource management, and system integration can produce a competitive autonomous robot within tight limitations.
E2.11 Dojo Drive
Sponsor: Ingram School of Engineering (ISoE)
Student Team: Jade Maldonado, Edwin Paniagua, Zachary Shannon
Faculty Advisor: Dr. Larry Larson
Dojo Drive is a competition-ready autonomous mini sumo robot built to intelligently search, detect, and overpower opponents in the arena. Using ultrasonic and infrared sensing, the robot identifies targets, avoids boundary lines, and dynamically adjusts its movement strategy in real time. Its dual-motor drivetrain delivers balanced speed and torque for both pushing and pulling events. Designed with a custom chassis and printed circuit board, the system emphasizes reliability, safety, and clean integration. Dojo Drive showcases advanced embedded systems design, control logic, and full subsystem coordination in a compact robotic platform.
E2.12 KongBot
Sponsor: Ingram School of Engineering (ISoE)
Student Team: John Anges, Josh Campbell, Stephan Cope
Faculty Advisor: Dr. Lawrence Larson
Our goal is to design and build an autonomous Sumo Bot that can identify its surroundings and strategically push rivals out of a given ring. We will deliver a fully functioning robot that integrates movement control, ring edge detection, and object detection with efficient power management. By maintaining a balance of motor power, sensor feedback, and program efficiency, our design allows for rapid response and continuous operation. In Senior Design 2, we will implement the custom PCB that we designed to replace breadboard-style wiring, improving reliability, organization, and overall system integration.
E2.13 ZillaBot
Sponsor: Ingram School of Engineering (ISoE)
Student Team: Roberto Macedo-Delgado, Eric Sides, Domingo Caban, Patrick Meurer
Faculty Advisor: Dr. Lawrence Larson
ZillaBot is a fully autonomous robot that detects opponents, locates boundaries, and pushes other robots out of an arena. ZillaBot also drags competitors across a pulling field in tug of war matches. It uses time-of-flight distance sensors, infrared boundary sensors, and fast decision logic to compete against other autonomous robots.
E2.14 Ripple Effect
Sponsor: Ingram School of Engineering (ISoE)
Student Team: Rion Lieberman, Aiden Olivarez, Gabriella Taverna, Ben Hoyt
Faculty Advisor: Dr. Marcelo Carvalho
Our project is a Wi-Fi sensing system designed to detect and identify movement using both current and past Wi-Fi protocols, allowing any space where Wi-Fi is deployed to be continuously monitored using non-Line-of-Sight methods. We will utilize channel state information (CSI) and machine learning algorithms to create a system that is learning and improving to enhance accuracy. Our system will require no additional installation, as Wi-Fi is already deployed in most modern environments. This can be used to detect both small and large-scale movements, such as home health monitoring, presence detection, and smart home device interfacing