Design and Development of an IR Sensor-Based Automated Guided Vehicle for Efficient Material Handling

Abstract

This research presents the design and development of an IR sensor-based Automated Guided Vehicle (AGV) prototype for efficient and autonomous material handling. The AGV is designed to navigate through multiple stations using IR sensors, eliminating the need for manual intervention in the pickup, assembly, and delivery of components. At each station, the AGV collaborates with robotic arms to pick a shaft and pulley, perform assembly, and transport the final package to the designated endpoint. The system uses a set of IR sensors for path following, station detection, and object verification, combined with an Arduino Mega controller and a relay-driven motor system. The solution offers a low-cost, reliable, and scalable automation system suitable for small-scale industrial applications.

Introduction

This project presents a compact, sensor-based Automated Guided Vehicle (AGV) designed to enhance industrial automation, particularly for automated material handling. The AGV uses IR sensors for path following and station detection and is integrated with robotic arms for tasks such as material pickup, assembly, and delivery, significantly reducing human intervention.

Key Points:

Problem Addressed:

Manual handling (e.g., forklifts, human labor) is inefficient, error-prone, and costly. The proposed AGV provides a flexible, autonomous, and efficient alternative.

Project Objectives:

• Navigate paths using IR sensors

• Stop at stations logically

• Interact with robotic arms

• Use sensors to confirm object handling

• Operate autonomously after initialization

Scope:

Applicable in:

• Manufacturing & Warehousing

• Material Handling

• Assembly & Packing

• Logistics & Distribution

Methodology Overview:

1. Design & Component Selection: Uses Arduino Mega, IR sensors, relay, and DC motors.

2. Mechanical Build: Chassis built for compact environments.

3. Sensor Integration: IR sensors for navigation, object/station detection; ultrasonic sensor for obstacle detection.

4. Programming: Arduino programmed to control logic, motor relay, and sequence actions.

5. Testing: Validated across 3 stations, refined for sensor accuracy and coordination.

AGV Operation:

1. Follows a defined path using IR sensors.

2. Stops at Station 1 – shaft picked up, pickup confirmed via IR.

3. Moves to Station 2 – pulley delivered, confirmed via IR.

4. Arrives at Station 3 – robotic arms assemble parts into a box.

5. Final station – box is dropped off, completing the automation cycle.

Main Components Used:

This AGV system serves as a cost-effective and scalable solution for small-scale industrial automation, improving workflow efficiency and reducing manual labor dependency.

Conclusion

The developed AGV prototype offers a practical and efficient approach to material handling and assembly using a sensor-driven autonomous platform. With the integration of IR sensors and robotic arms, the system effectively reduces human involvement, improves accuracy, and increases workflow speed. Its modular design allows easy scaling and customization for various industrial setups. This IR-based solution is especially suited for small-to-medium automation tasks where low cost, simplicity, and flexibility are key.

References

[1] N. Viswanadham, “Automation in Material Handling Systems,” Journal of Manufacturing Systems, Vol. 25, No. 2, 2020. [2] R. Gupta and A. Singh, “Design and Implementation of Line Following AGV Using IR Sensors,” International Journal of Robotics and Automation, 2021. [3] S. Kumar et al., “Smart Material Handling Using Arduino Based Autonomous Robot,” International Research Journal of Engineering and Technology, Vol. 6, No. 4, 2022. [4] D. Patel, “Development of a Cost-Effective AGV for Industrial Applications,” International Journal of Engineering Research, 2023. [5] A. Sharma, “Path Following Robot for Assembly Line Using Sensor-Based Control,” IEEE Conference on Industrial Automation and Robotics, 2024.

Copyright

Copyright © 2025 Prof. H. K. Shete, Anup Revansiddha Konapure, Aniket Ananda Jadhav, Aditya Anil Varude, Pushkar Rajendrakumar Raut, Parag Hemant Burande. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.