International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 8 Issue 2
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Robotic Waste Detection and Segregation System Using Computer Vision And ROS2

Author(s) Ms. Ayushi Gupta, Ms. Richa Sunil Dixit, Mr. Atif Shah, Ms. Janhavi Prashant Bartakke, Prof. Puja Johari
Country India
Abstract The municipal solid waste generated by world annually is 2.01 billion tonnes, averaging 0.74 kg per person per day. India produces between 159,000 and 170,338 tonnes of solid waste, which is expected to double by 2025. Moreover, approximately 35%–40% of urban municipal solid waste consists of dry recyclable materials. There are many developed countries that use advanced robotic machinery for waste segregation, while many other regions still rely on human workers. These traditional manual methods could be extremely slow, error-prone and hazardous to workers, who are consistently exposed to toxic, biomedical, and sharp materials. To address these problems, this paper presents a low-cost autonomous robotic waste detection and segregation system that use integrates Robot Operating System 2 (ROS 2), a Raspberry Pi, a Raspberry Camera Module, and a 5-degree-of-freedom robotic arm. Furthermore, a well-tuned YOLOv8 deep learning model is used for real-time, multi-class waste detection and classification. A ROS2 nodes manage multiple component such as perception, actuation and decision making in real time. The proposed system provides a practical, affordable, and scalable solution for intelligent waste management, suitable for smart city infrastructure and resource-constrained urban environments.
Keywords Waste Segregation, ROS2, YOLOv8, Deep Learning
Field Engineering
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-04-14
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.74664
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E-ISSN 2582-2160
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