Spring 2022

Spring 2022 Muhammad Rashid Best Project Award winners.

Spring 2022 First Place

RoboBoat: An Autonomous Surface Vessel

Team Members
Charles Hostick, Shane Imm, Erik LaBrot, John Osmialowski, Thati Vang, and Aaron Rogers

Faculty Mentor
Dr. Tarek Youssef Elsayed

Abstract
The UWF Marine Robotics Team designed, built, and programmed an autonomous unmanned boat.  The robotic boat uses a combination of GPS, LiDAR, IMU, and cameras to detect and navigate its environment.  The environmental data is fed into the Robotic Operating System (ROS) where the sensor data is used to determine a navigation solution.  A unique feature of this project is the ability to move the boathouse that houses the systems and sensors needed for autonomous operations between a boat and tank platform.  This capability allows the team to test both on land and in water.  The land testing option will increase the amount of time we can spend testing the vehicle.  The team was able to achieve basic autonomous waypoint navigation to include object detection and avoidance.

Spring 2022 Second Place #1

"Forget Me Not" Car Seat Safety Monitor

Team Members
Kyle Stipp, Christian Clark, Jess Keith, Gabriel Roura, Matthew Fulton, Kyle Parker

Faculty Mentor
Dr. Yazan Alqudah

Abstract
There are many child alert systems to choose from currently on the market. Most fall in the spectrum of lackluster reliability all the way to unavoidable annoyance; both result in reduced use and are self-defeating. The system must be reliable and there are times when a child is intentionally left in a car, for example when the parent is filling the vehicle with fuel. This system is designed to have reliable detection of a child, and methods to override the alerts; however, it still monitors for hazardous conditions when overridden. Many children perish or are exposed to heat injury every year and this system aims to prevent them while being affordable, reliable, and user-friendly.

Spring 2022 Second Place #2

Automated Batch Weighing System

Team Members 
Carly Ritchie, Hanna Larmore, Cristy Higginbotham, Samara Potter, Bruno Ariza

Faculty Mentor
Dr. Tarek Youssef Elsayed

Abstract
The Auto Batch System is an automated machine that dispenses dry powdered/granular materials such as flour and sugar into a bowl while also weighing said ingredients within 97.5% accuracy. The bowl is first placed on a scale, which will measure the materials dispensed, the scale with the bowl is attached to a conveyor. Once the user types in the amount and the various types of materials needed (i.e. 36 grams of flour) the conveyor will move to the required stations where the materials are dispensed into the bowl. There are three containers in the system so each recipe can have a variation of three different materials. Using the screwable cap on the top of the container a user can easily refill or change the material to what they want; while also needing to update the app to account for the change. This system is designed for a home setting but is scalable for an industrial setting as well. The Auto Batch System's purpose is to reduce worker shortages and limit contact with potentially hazardous materials.