Mini-Rover Project

Overview

This project is a mini-rover built on a Raspberry Pi 5, using the CoKoino chassis and the CoKoino Pi Power 4WD Hat. The rover is controlled remotely via an Android gamepad app over a TCP socket connection. Additionally, it is equipped with a PiCamera, enabling real-time video streaming over a UDP socket.

Features

• Remote control using an Android gamepad app
• Real-time video streaming from the PiCamera over UDP
• 4WD motor control via the CoKoino Pi Power 4WD Hat
• Bidirectional communication using TCP and UDP sockets
• Python-based implementation with modular and scalable design

Hardware Components

• Raspberry Pi 5
• CoKoino Chassis
• CoKoino Pi Power 4WD Hat
• PiCamera (Raspberry Pi Camera Module)
• Android device (for gamepad app control)
• Power supply and battery pack

Software Components

• Python for the rover control logic
• Sockets (TCP & UDP) for communication
• OpenCV & libcamera for video streaming
• PyBluez for Bluetooth functionalities (work in progress)

System Architecture

1. Gamepad App (Android Device):
   • Sends control commands over a TCP socket to the rover
2. Raspberry Pi (Rover Controller):
   • Receives commands and translates them into motor actions
   • Streams video from the PiCamera over UDP socket
3. Communication:
   • TCP Socket: Used for receiving control inputs
   • UDP Socket: Used for low-latency video streaming

Installation

1. Set up the Raspberry Pi

sudo apt update && sudo apt upgrade -y
sudo apt install python3-pip

2. Clone the Repository

git clone https://github.com/Cyrus243/PathFinder.git
cd pathFinder

3. Install Dependencies

pip install -r requirements.txt

4. Run the Rover Control Server

python3 main.py

Usage

1. Start the rover server to accept control commands.
2. Start the video stream for real-time monitoring.
3. Connect the Android gamepad app to the Raspberry Pi's IP over TCP.
4. Control the rover remotely and view the live camera feed.

Future Improvements

• Introduce BLE control configuration as an alternative communication method
• Improve video streaming performance using optimized encoding
• Implement computer vision-based obstacle avoidance
• Add autonomous navigation modes

License

This project is licensed under the MIT License.

Author

Aaron Mbala