Design and development of guidance algorithm based on optical and QR image system in automated guided vehicle

This study aims to improve the flexibility and adaptability of Automated Guided Vehicles (AGVs) by developing a guidance algorithm that combines line-following capability with QR code-based navigation. The AGV prototype is equipped with an infrared sensor array for detecting path lines, managed by a Proportional-Derivative (PD) control system to maintain alignment. A GM65 barcode reader is used to scan QR codes placed along the floor, which contain directional and positional data. A Raspberry Pi Zero handles processing and communication tasks, enabling wireless operation. The QR codes serve as auto-identifiers to help the AGV make real-time navigation decisions. Testing showed that the AGV could navigate various routes with an average success rate of 88.4%. The prototype reached an average speed of 0.142 m/s on straight paths, 0.082 m/s on curves with a 10 cm radius, and 1.05 m/s during 90-degree turns. The QR code reader successfully identified codes within an average of 1.62 seconds. Integrating optical sensors and QR code recognition into AGV navigation provides a more flexible and scalable solution compared to conventional methods, particularly in environments that often require changes in layout or routing. The research results are novel and introduce an innovative approach to Automated Guided Vehicle (AGV) navigation by integrating optical sensors and QR codes, offering a more flexible guidance system compared to conventional methods. This approach offers a cost-effective and adaptable alternative for use in industrial automation systems, such as warehouses or production facilities. Further improvements could include route optimization algorithms like Dijkstra's and enhanced control systems for better precision and reliability.