A vision-based autonomous mobile robot navigates based on detected directional aids and Aruco markers using OpenCV for warehouse inventory management to successfully identify and count defective and non-defective inventory at each station.
The robot relies on directional aids, such as triangular markers, strategically placed throughout the warehouse to make informed turn decisions. By following these directional aids, the robot will seamlessly manoeuvre through the warehouse's three major sections, ensuring it covers the entire inventory space. Upon reaching this destination, the robot will display the final count of defective and non-defective widgets, providing warehouse management with accurate and up-to-date inventory information.
The project demonstrates the practical application of computer vision techniques for marker detection and shape recognition. The system can be used in various real-world scenarios by combining the functionalities of detecting ArUco markers and triangles. For example, it could be integrated into robotics or automation systems to provide visual feedback and make decisions based on detected markers or triangle orientations.
The project's implementation successfully achieves its objectives of real-time marker detection and triangle direction determination. However, there are potential areas for improvement. For instance, the code could be optimized for better performance, such as using multi-threading to parallelize the detection processes. Additionally, more robust error handling and exception management could be incorporated to enhance the reliability and stability of the system.