Paper Key : IRJ************190
Author: Abhisheik Muralidharan Nair,Amina Ahemad,Roshan Mathew Varghese,Rony Mathew Antony,Kurien Thampy
Date Published: 23 Mar 2025
Abstract
The integration of advanced robotic systems has been a promising approach toward the realization of sustainable waste management solutions. In this line, the present paper deals in detail with the design, development, and implementation of a robotic arm system dedicated for efficient waste segregation. The system proposed in the present paper takes advantage of state-of-the-art sensors and machine learning algorithms, combined with the techniques of robotic manipulation, for the identification, classification, and sorting of waste materials with accuracy.High-resolution cameras and spectroscopic sensors are incorporated into the robotic arm to analyze the makeup of items thrown in the refuse. The wide variety of manipulation capabilities for this robotic arm is optimized for the handling of the widest possible variety of thrown-away objects, ensuring minimal damage during handling and efficient sorting. Machine-learning models, rigorously trained on large datasets of various types of refuse, enable it to sort materials with extremely high accuracy into categories of recyclable, non-recyclable, and hazardous.The main contributions of this research were to develop a robust waste recognition algorithm, a scalable robotic arm design that would fit into different scenarios in waste management, and a real-time control system so that it could work seamlessly. Experimental results show that the proposed system significantly enhances segregating wastes in terms of both time and accuracy compared to traditional manual methods.These findings prove that robotic arms can change the face of strategies related to waste handling and, thus, contribute to environmental sustainability while reducing exposure to hazardous wastes for humans. Future work involves increased adaptability of the system with respect to different streams of waste, and becoming part of larger automated facilities for waste processing.