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This paper presents the design and development of an autonomous robotic system for cleaning the surface of water bodies while simultaneously segregating waste. The robot utilizes a conveyor belt mechanism to efficiently collect floating debris. Collected waste is then processed through an onboard machine learning model, enabling real-time segregation of waste materials. A GPS module provides location tracking, allowing for remote monitoring and control of the robot. The paper details the mechanical design, control systems, and machine learning algorithms employed. Additionally, the effectiveness of the system is evaluated through field tests, demonstrating its potential for automated and sustainable waste management in aquatic environments.

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Hydrobot: An Autonomous Solution for Surface-Level Aquatic Debris Collection and Sorting Using Convolutional Neural Networks (CNN)

  • 会议论文

  • 发表日期：

    2026年

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  • M. K. Deshmukh,
  • Tejaram Chaudhari,
  • Aditya Joshi,
  • Shon Gaikwad,
  • Abhinav Shukla,
  • Samyak Dhole

摘要

This paper presents the design and development of an autonomous robotic system for cleaning the surface of water bodies while simultaneously segregating waste. The robot utilizes a conveyor belt mechanism to efficiently collect floating debris. Collected waste is then processed through an onboard machine learning model, enabling real-time segregation of waste materials. A GPS module provides location tracking, allowing for remote monitoring and control of the robot. The paper details the mechanical design, control systems, and machine learning algorithms employed. Additionally, the effectiveness of the system is evaluated through field tests, demonstrating its potential for automated and sustainable waste management in aquatic environments.