Assembly Automation & AGV-Assisted Material Handling

Abstract

Manual assembly processes often present significant challenges, including time inefficiency, human error, and safety risks particularly when handling small or delicate components. To address these issues, this research presents the development of a Raspberry Pi-controlled robotic arm prototype, specifically designed for assembly operations in a mechatronics laboratory setting. The system collaborates with an Automated Guided Vehicle (AGV) to create a semi-autonomous assembly line. Positioned at a designated workstation, the robotic arm performs precision assembly of 3D printed parts and transfers the completed units onto the AGV for further processing. Integration of sensors ensures the timely detection of the AGV’s arrival, enabling seamless coordination between the robotic arm and the mobile platform. This prototype aims to automate repetitive tasks, minimize human intervention, and enhance safety and productivity. Future industrial adaptations of this system are expected to further streamline manufacturing processes and reduce operational risks.

Introduction

The growing need for precision, speed, and safety in manufacturing has led to increased automation, especially through robotic systems that reduce manual labor and errors. This project involves designing a Raspberry Pi-controlled robotic arm integrated with an Automated Guided Vehicle (AGV) to simulate an automated assembly system suitable for smart manufacturing. The system uses sensors for coordination between the robotic arm and AGV, aiming for scalable industrial applications.

The robotic arm autonomously performs assembly tasks and collaborates with the AGV by detecting its arrival via sensors. Controlled by a Raspberry Pi, the system uses servo motors and real-time visual feedback from an R-Pi camera for precise movements. Key components include the Raspberry Pi 4, control card, robotic arm kit, power supply and module, and camera.

Objectives include developing a fully automated 6-degree-of-freedom (6DOF) robotic arm capable of picking, placing, and assembling parts with AGV coordination. The system aims to speed up assembly, reduce manual labor, and improve efficiency.

The robotic arm combines stepper and DC motors for precise control, machine learning (using TensorFlow) for object detection, and an Arduino-based control system with a Python interface. Despite challenges like dynamic environments and limited training data, the system shows strong potential for applications across various industries, enhancing productivity and reducing labor costs.

Conclusion

In conclusion, the project exhibits significant potential for industrial integration, offering precision and efficiency in small-scale applications such as medical devices, 3D printing assemblies, and fine component manufacturing. Simultaneously, its scalability and adaptability position it as a valuable asset in large-scale sectors including automotive production, electronics assembly, and warehouse automation.

References

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Copyright

Copyright © 2025 Dr. S. D. Bagade, Siddharth Kalburgi, Tanmay More, Shreya Patil, Akash Mohite, Omkar Jadhav. 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.