Result: Implementing Autonomous Rover Control Using NASA's F Prime Framework and YOLO-Based Image Processing on Raspberry Pi 5

Recent developments in autonomous rover systems have been challenged with reliable deployment, low-latency perception, and control stacks on resource-constrained edge platforms. This project presents the development and implementation of an autonomous rover control system utilizing NASA's F Prime software integrated with YOLO-based real-time object detection, executed on Raspberry Pi 5. The rover uses Raspberry Pi, which contains the standalone Python code for object detection, by leveraging Ultralytics' YOLO11n model and Google's MediaPipe for detections on edge devices. These Machine Learning models can detect, classify, and identify objects within its operational environment. The computer, connected to the same network as the Raspberry Pi, operates as a UDP server that sends specific commands to initiate or stop the Pi's object detection and movement routines. The Pi serves as a UDP client, continuously listening to upcoming UDP commands. The Pi utilizes the F Prime framework's component to control the rover remotely using the F software. This design enables responsive, accurate, and reliable control of the autonomous rover, thus demonstrating potential for real-world applications such as environmental exploration, surveillance, disaster response, and remote monitoring.