Line Following Robot Computer Vision Project

Project Title: Line Following Robot with TensorFlow on Raspberry Pi 4

Objective: Design and implement a line-following robot that utilizes machine learning, specifically TensorFlow, to navigate along a predefined path. The project involves training a neural network model using a dataset of 1500 images capturing various scenarios of the robot following a line. The trained model will be deployed onto a Raspberry Pi 4 for real-time inference and control of the robot's movements.

Project Components:

1. Dataset Collection:

   • Capture a diverse set of images featuring the robot following a line under different conditions (lighting, surface variations, angles).
   • Annotate the images to label the position of the line, providing supervised training data.

2. TensorFlow Model Training:

   • Use TensorFlow to create and train a convolutional neural network (CNN) for image classification.
   • Split the dataset into training and validation sets to evaluate the model's performance.
   • Optimize the model for inference on resource-constrained devices like the Raspberry Pi.

3. Model Evaluation:

   • Assess the trained model's accuracy and performance using the validation set.
   • Fine-tune the model if necessary to improve its ability to generalize to different conditions.

4. Raspberry Pi Deployment:

   • Set up the Raspberry Pi 4 with the necessary dependencies, including TensorFlow.
   • Load the trained model onto the Raspberry Pi for real-time inference.
   • Interface the Raspberry Pi with the robot's control system.

5. Robot Control:

   • Implement a control algorithm that interprets the model's predictions to guide the robot's movements along the line.
   • Fine-tune the control parameters for smooth and accurate line following.

6. Testing and Optimization:

   • Test the line-following robot in various environments to ensure robust performance.
   • Optimize the system for speed, responsiveness, and adaptability to different line-following scenarios.

7. Documentation and Presentation:

   • Document the entire project, including the dataset creation process, model architecture, training procedure, Raspberry Pi setup, and robot control logic.
   • Prepare a presentation summarizing the project's goals, methodology, challenges faced, and results achieved.

Expected Outcomes:

• A functional line-following robot capable of following a line in different conditions.
• A trained TensorFlow model optimized for deployment on the Raspberry Pi.
• Comprehensive documentation for future reference or replication.

Skills Developed:

• Image data collection and annotation.
• TensorFlow and machine learning for robotics.
• Embedded system deployment on Raspberry Pi.
• Algorithm development for real-time control.

Potential Challenges:

• Lighting and environmental conditions affecting the model's performance.
• Fine-tuning control parameters for optimal robot navigation.
• Addressing hardware limitations on the Raspberry Pi for real-time inference.

Future Enhancements:

• Explore the possibility of incorporating additional sensors (e.g., ultrasonic sensors) for obstacle avoidance.
• Implement a more advanced neural network architecture for improved performance.

This project provides an excellent opportunity to combine machine learning, robotics, and embedded systems, offering a hands-on experience in building an intelligent and autonomous robot. Good luck with your project!