evecv | Object Detection Robot with Raspberry Pi Zero 2W

This project aims to build a robot capable of performing object detection using a Raspberry Pi Zero 2W and a camera module. The goal is to detect and track the user's presence in the camera feed.

Requirements

To replicate this project, you will need the following hardware and software:

Hardware

• Raspberry Pi Zero 2W
• Camera module compatible with Raspberry Pi
• MicroSD card (8GB or larger)
• Power supply for Raspberry Pi
• Wheels, motors, and chassis for the robot
• Breadboard and jumper wires for circuit connections

Software

• Raspbian or Raspberry Pi OS installed on the Raspberry Pi
• Python 3
• TensorFlow Lite
• OpenCV
• GPIO Zero

Setup

1. Connect the camera module to the Raspberry Pi Zero 2W.
2. Install the necessary software libraries and dependencies on the Raspberry Pi.
3. Clone or download the project repository to the Raspberry Pi.

Configuration

Before running the project, you need to configure a few parameters:

1. Adjust the camera settings in the code to suit your environment (e.g., resolution, frame rate).
2. Customize the object detection model to detect your presence.
3. Calibrate the robot's motors and wheel encoders to ensure accurate movement.

Usage

1. Connect the Raspberry Pi Zero 2W to power.
2. Run the main script on the Raspberry Pi.
3. The robot will start detecting and tracking the user in the camera feed.

Troubleshooting

If you encounter any issues during setup or operation, consider the following:

• Double-check your hardware connections.
• Ensure that the required libraries and dependencies are installed correctly.
• Check for any error messages or logs generated by the application.

Future Improvements

Here are some ideas for future enhancements to this project:

• Implement voice commands for controlling the robot.
• Integrate additional sensors for environment mapping and obstacle avoidance.
• Explore cloud connectivity for remote monitoring and control.

License

This project is licensed under the MIT License.

Acknowledgments

• The TensorFlow Lite Object Detection Model infrastructure by Evan Juras served as the foundation for this project.
• Special thanks to the open-source community for their contributions and support.