Blue Color Line Following Car Using Computer Vision

This project presents the development of a blue line-following car with obstacle detection capabilities using computer vision, image processing, and path planning techniques. The principal objectives of this project are to develop a car that can accurately follow a blue line, implement obstacle detection using ultrasonic sensors, explore color-based segmentation and contour detection for line following, and utilize the Rapidly-Exploring Random Tree (RRT) algorithm for efficient path planning and obstacle avoidance. The methods employed include image capture, color space conversion (BGR to HSV), binary mask creation, contour detection, centroid calculation, motor speed control, ultrasonic sensor-based obstacle detection, and the RRT algorithm for path planning.The experimental results demonstrate the successful implementation of the car, which smoothly navigates along the track, handles curves, and avoids obstacles using the RRT algorithm. The ultrasonic sensors effectively detect obstacles, and the car takes appropriate actions to avoid collisions. The main conclusions drawn from this project highlight the effectiveness of integrating computer vision, obstacle detection, and path planning techniques to enable intelligent and reliable navigation in line-following robots. The project serves as a foundation for further research and development in the field of autonomous robotics, showcasing the potential of combining various technologies to achieve robust and efficient navigation systems.