Multifunction Programmable Automation Software Application for Robotic Arm

Abstract

This paper explores the intersection of robotics and computer vision, focusing on developing a robotic arm capable of recognizing and interacting with objects based on their color and shape—much like human vision. The process starts with selecting or designing a robotic arm equipped with high-resolution cameras and advanced sensors.These components enable the arm to gather detailed visual data, forming the foundation of its decision-making abilities.At the core of this system is image processing. Cutting-edge algorithms analyze raw visual input, detecting colors and shapes in real time. This allows the robotic arm to respond dynamically rather than simply executing preprogrammed tasks. To bridge perception and action, sophisticated control algorithms areimplemented.These guide precise movements for tasks like object sorting, pick-and-place operations, and quality control in industrial settings. The potential applications of this technology span manufacturing, logistics, agriculture, healthcare,andeducation.Byseamlesslyintegratingroboticswithcomputervision,This paperpaves the way for intelligent automation and enhanced human-robot collaboration.

Introduction

The paper discusses the development of an intelligent robotic arm system designed for industrial object detection and sorting based on color and shape. Using HSV-based color detection and contour-based shape recognition with OpenCV, the system autonomously identifies and sorts objects such as squares, triangles, and rectangles. This automation addresses inefficiencies of manual sorting, especially due to human inconsistencies in color perception.

A multifunction programmable software with a user-friendly GUI allows operators without coding skills to set sorting parameters and reconfigure tasks quickly, improving flexibility and reducing downtime. The system integrates hardware components including actuators, sensors, cameras, and motor controllers to execute precise pick-and-place operations.

Extensive literature review highlights the growing use of image processing in robotics across industries like manufacturing, healthcare, agriculture, and logistics. The methodology section details the image capture and processing steps, emphasizing the use of HSV color space for robust color detection and contour analysis for shape classification.

Experimental results show high accuracy in color (98%) and shape detection (95%), fast processing times (~3.5 seconds per object), and strong adaptability to varying lighting conditions. Compared to manual and RGB-based automated sorting, the system offers significant efficiency improvements.

Overall, the paper demonstrates how combining advanced vision algorithms with programmable robotic arms can enhance industrial automation, reduce human error, and increase production efficiency.

Conclusion

Thispaperpresentedamultifunctionalrobotic arm system capable of detecting and sorting objectsbasedoncolorandshapeusingHSV- based color detection and contour-based shape recognition techniques. The experimental results confirmed the system\'s high accuracy, efficiency, and adaptability, making it a viable solution for industrial automation. Thekeyfindingsofthestudyinclude: • Ahighaccuracyrate of98%forcolor detection and 95% for shape recognition. • Anaverageobjectprocessingtimeof 3.5 seconds, enabling rapid sorting operations. • Robust performance across varying lighting conditions, demonstrating system adaptability. • A user-friendly software interface allowing easy reconfiguration of sorting parameters. Theimplementationofthisroboticsystemhas significantimplicationsforindustriessuchas manufacturing, packaging, and logistics, where automated sorting can reduce errors andenhanceefficiency.Futureenhancements may focus on integrating deep learning models for more complex object recognition tasks, improving real-time decision-making, andexpandingtherange ofdetectableshapes and colors. Additionally, implementing a reinforcement learning framework could optimize the robotic arm’s movement for improved speed and precision.

References

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Copyright

Copyright © 2025 Dr. Nita Thakare, Mrunmayee Atey, Lochan Futane, Sakshi Menkudale . 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.