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April 29, 20260 citationsOpen Access

Roboclean: Automated Garbage Collection With Conveyor Mechanism

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DRDr. P Ramana ReddyPNPothireddy NithishaSPSakamuru Hari priya

Key Points

  • To develop an automated garbage collection system for floating waste in water bodies using a conveyor mechanism.
  • Designed an ESP32-based system with dual conveyor belts driven by DC motors.
  • Utilized mobile application for real-time remote control and monitoring via IoT.
  • Included an LCD display for on-site status information.
  • System reduces manual labor by automating waste collection.
  • Improves safety and enhances cleanliness of water bodies.
  • Cost-effective and scalable solution for environmental management.

Abstract

Increasing water pollution due to floating solid waste in rivers, lakes, and drainage canals has become a major environmental concern, and manual waste collection in water bodies is inefficient, unsafe, and time-consuming. This project presents Roboclean, an ESP32-based automated garbage collection system designed specifically for collecting floating waste from water surfaces using a conveyor belt mechanism. The system employs dual conveyor belts driven by DC motors through motor driver modules to lift and transfer waste from water to a collection bin. An ESP32 microcontroller acts as the central control unit, coordinating motor operations and system monitoring. IoT connectivity using the Blynk platform enables real-time remote control and monitoring of the system through a mobile application. A 16×2 LCD display provides on-site status information, while a regulated power supply ensures reliable operation. By automating floating waste collection and enabling remote supervision, the proposed system reduces manual labor, improves safety, and enhances cleanliness of water bodies. Roboclean offers a cost-effective and scalable solution suitable for rivers, lakes, sewage canals, and smart city environmental management applications.

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Cite This Study

Reddy et al. (2026) studied this question.

synapsesocial.com/papers/69f1545d879cb923c49448c8https://doi.org/10.5281/zenodo.19809779
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