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A state-of-the-art review on robotics in waste sorting: scope and challenges

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Abstract

An essential component of a waste management system is waste sorting. Correct sorting of waste is crucial for creating a clean environment for everyone, reducing pollution and reusing recyclable materials. Manual waste sorting can cause serious health issues to the workers due to various disease-causing agents present in the garbage. The use of robots in sorting of materials such as glass, paper, plastic, metals, etc., from other waste can facilitate the production of secondary raw materials as well as conserve energy and production costs. Robots can help in efficient sorting of waste and can work endlessly thus eliminating health hazard to labours. By using computer vision, artificial intelligence, and automation systems, both, the efficiency and accuracy of waste sorting can be increased. This paper showcases the recent studies related with implementation of robots in waste sorting for efficient recycling, algorithms, types of grippers and its advancements. Various studies have been discussed related to computer vision and comparison of various algorithms is also presented. Major challenges faced in implementation of robotic sorting on global scale and its future scope has also been discussed.

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Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

IoT:

Internet of Things

IR:

Infrared

ML:

Machine learning

DL:

Deep learning

CNN:

Convolution Neural Network

R-CNN:

Region-based Convolution Neural Network

SSD:

Single Shot Multiproxy Detector

FCOS:

Fully Convolutional One-Stage object detection

KNN:

K nearest neighbour

SVM:

Support Vector Machine

SLAM:

Simultaneous Localization and Mapping

IoT:

Internet of Things

Dof:

Degree of freedom

SUS:

System Usability Scale

COCO dataset:

‘Common Objects in Context’ dataset

MAP:

Mean Average Precision

PET:

Polyethylene terephthalate

HDPE:

High Density Polyethylene

LDPE:

Low Density Polyethylene

PP:

Polypropylene

PS:

Polystyrene

GUI:

Graphical User Interface

LiDAR:

Light Detection and Ranging

DOF:

Degree of freedom

MSWM:

Municipal Solid Waste Management

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  1. School of Mechanical Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune, 411038, Maharashtra, India

    Anushka G. Satav, Sunidhi Kubade, Chinmay Amrutkar, Gaurav Arya & Ashish Pawar

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The first draft of the manuscript was written by AS, CA and AP. AS, GA and SK performed the conceptualization of the research idea and reviewed the edited manuscript. All authors have made a substantial contribution to the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ashish Pawar.

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Satav, A.G., Kubade, S., Amrutkar, C. et al. A state-of-the-art review on robotics in waste sorting: scope and challenges. Int J Interact Des Manuf 17, 2789–2806 (2023). https://doi.org/10.1007/s12008-023-01320-w

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