Sports Field Marking Robot

Accurate field marking is crucial for sports facilities, but traditional methods are labor intensive, time consuming, and prone to human error. This project presents an autonomous sports field marking robot designed to optimize accuracy and efficiency by integrating user input with real-time depth detection. The system utilizes OpenCV-based computer vision techniques to detect a reference object, ensuring precise alignment and field calibration. The robot dynamically adjusts its trajectory using depth optimization, reducing inconsistencies in field markings. A user-friendly interface allows operators to select different field layouts, enabling adaptability for various sports. The autonomous navigation system employs sensor fusion, integrating GPS, IMU, and computer vision for path planning and obstacle avoidance. The paint application mechanism ensures uniform line thickness and accurate boundary placement. By automating the marking process, this solution minimizes human intervention, lowers operational costs, and enhances the reliability of field dimensions. Compared to conventional manual methods, the proposed system significantly improves accuracy and repeatability. Future enhancements could integrate AI-driven decision making for optimized path planning and increased automation in multisport configurations. This research highlights the potential of robotics and vision-based depth sensing in revolutionizing sports field maintenance, offering a scalable and adaptable solution for modern sports facilities.