Title: Enhancing recycling efficiency: a SCARA robot and CNN model for waste segregation

Authors: Gokul Jagadish; S. Abhishek; Amal Prakash; Arjun R. Nair

Addresses: Department of Mechanical Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India ' Department of Mechanical Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India ' Department of Mechanical Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India ' Department of Mechanical Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India

Abstract: The development of waste sorting robots, particularly the SCARA robot, has shown promising results in automating the recycling process. By utilising convolutional neural networks (CNNs) for image classification, the SCARA robot can accurately differentiate between recyclable and non-recyclable materials. This technology has the potential to significantly improve waste management practices, enhancing efficiency and accuracy in waste segregation. Future research could focus on integrating additional sensors and implementing advanced algorithms to further optimise the sorting process. Additionally, exploring the potential of deep learning techniques to classify a broader range of refuse materials could further enhance the performance of waste sorting systems. The SCARA robot's design, which includes a four-DOF base and the use of 3D printing technology, has been carefully analysed and chosen for optimum output and manufacturing simplicity, contributing to its reliability and durability.

Keywords: SCARA; waste-sorting; CNN; recycling; deep learning.

DOI: 10.1504/IJAMECHS.2024.143147

International Journal of Advanced Mechatronic Systems, 2024 Vol.11 No.3, pp.145 - 153

Received: 05 Oct 2023
Accepted: 08 Apr 2024
Published online: 04 Dec 2024 *

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