We will utilize the OpenAirInterface (“OAI”) Radio Access Network (“RAN”) stack and the OAI 5G Core stack. These projects are open source and allow engineers to implement their own private 5G controllers, base stations, and antennas. These stacks are primarily developed using the C programming language. The implementation of these two stacks will be our primary goal and demand the most time. To implement the RAN stack, we must have a virtual machine installed in our computers in order to download the UE software and simulated hardware on the OpenAirInterface 5G Core webpage. We will contact UConn ITS to help install RedHat onto our computers by mid-November. After downloading the software needed, we must set up the channel emulator. The setup is a multi-step process that, when established allows us to run simulations and test different real-world scenarios that affect the network's performance. We must have a working basic network topology by the end of the fall semester to confirm its effectiveness to continue with gathering metrics of the prototype network. Our CV camera will ideally be implemented using the OpenCV library, which can be used with Python or C++. Since the focus is on 5G networking, the CV implementation should use existing solutions and algorithms as much as possible. The CV only needs to facilitate basic perception of the robot's location and surroundings. The robot will be an off-the-shelf kit using an Arduino or Raspberry Pi computing board. We will build a simple course that demonstrates the robot’s ability to navigate its environment thanks to the CV camera connected to the 5G network. These details will be further fleshed out as we prototype our network and choose the features that we want to showcase the most. Working with the computing board will take place in the beginning of the 2025 spring semester to get the necessary measurements from the benchmarking utilities earlier mentioned.