Projects
NASA Minds 2024 Winners
NASA Minds is an annual competition that challenges over 40 schools to solve technical issues related to the Artemis mission. Participants develop Conceptual Design Reports (CDR), Preliminary Design Reports (PDR), and technical papers. Successful teams receive NASA funding to build their designs and present them to a panel of NASA engineers. I played a leadership role in the REALM (RFID-Enabled Autonomous Logistics Management) project, overseeing both the detailed design and high-level architecture of our inventory tracking system. The design, driven by a limited budget and the technical skills of community college students, needed to meet NASA's requirements.
Thrust Vectoring Rocket
I held a leadership role in driving the development of our thrust vectoring rocket project for the Engineering Club at Fullerton College.
This initiative centered around a rocket with real-time thrust control, facilitated by an Arduino Nano microcontroller. The project's technical sophistication required the development of a custom PCB designed for sensor integration, which conferred a heightened degree of stability and control during flight. Our comprehensive sensor array included a barometer for altitude measurement, an accelerometer for orientation tracking, and servo mechanisms for precise thrust modulation.
SLAM Bot
I assumed the primary leadership role in the development of the 'Slam Bot' project. The 'Slam Bot' represents a 2D mapping robotic system leveraging a Lidar sensor for the purpose of mapping terrain surfaces and implementing obstacle avoidance strategies through the utilization of the Lidar-derived map. The 'Slam Bot' was integrated with the Robot Operating System (ROS) to facilitate the synchronization of data streams originating from the microcontroller, sensor suite, and Raspberry Pi 3.
Future enhancements are slated to incorporate advanced features, notably the integration of computer vision capabilities and the addition of a camera system. These enhancements will empower the robot to detect and track objects by utilizing data from both the camera and Lidar sensors.
