This project demonstrates an automated guided vehicle's ability to follow paths and classify stocks by color, implying advances in warehouse automation and logistics.
This study presents the design and implementation of a line-following Automated Guided Vehicle (AGV) for stock distribution using color detection. The AGV incorporates mechanical, electrical, and control engineering principles to improve warehouse automation. The primary goal is to develop an AGV that autonomously follows a designated path and sorts stocks by color. This project enhances students’ understanding of mechatronics system design by integrating fabrication, sensor integration, embedded programming, and control system optimization. The methodology includes system modeling, hardware selection, circuit design, algorithm implementation, and experimental validation. The AGV’s performance was assessed under different conditions to evaluate accuracy, stability, and adaptability. The AGV successfully follows predefined paths and accurately classifies stocks by color, optimizing logistics and automation processes. Experimental and simulation data confirm the system’s effectiveness. This project establishes a fundamental framework for AGV-based logistics and contributes to the advancement of autonomous warehouse solutions.
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Huynh et al. (2025) studied this question.
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