ArXiv TLDR

An Intelligent Robotic and Bio-Digestor Framework for Smart Waste Management

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2604.14882

Radhika Khatri, Adit Tewari, Nikhil Sharma, M. B. Srinivas

cs.ROcs.LG

TLDR

This paper introduces an intelligent robotic and bio-digestor framework for automated waste segregation and optimized biological conversion.

Key contributions

  • Integrates a robotic arm (MyCobot 280, YOLOv8, ROS) for real-time waste segregation with 98% accuracy.
  • Features a sensor-equipped bio-digestor that monitors temperature, pH, pressure, and motor RPM.
  • Uses PSO and a regression model to dynamically optimize bio-digestor parameters for efficiency.
  • Provides a scalable, intelligent, and practical framework for smart waste management.

Why it matters

This framework addresses critical challenges in municipal solid waste management by offering an automated and intelligent solution. It significantly reduces manual intervention and maximizes waste conversion efficiency, making it highly relevant for sustainable urban development.

Original Abstract

Rapid urbanization and continuous population growth have made municipal solid waste management increasingly challenging. These challenges highlight the need for smarter and automated waste management solutions. This paper presents the design and evaluation of an integrated waste management framework that combines two connected systems, a robotic waste segregation module and an optimized bio-digestor. The robotic waste segregation system uses a MyCobot 280 Jetson Nano robotic arm along with YOLOv8 object detection and robot operating system (ROS)-based path planning to identify and sort waste in real time. It classifies waste into four different categories with high precision, reducing the need for manual intervention. After segregation, the biodegradable waste is transferred to a bio-digestor system equipped with multiple sensors. These sensors continuously monitor key parameters, including temperature, pH, pressure, and motor revolutions per minute. The Particle Swarm Optimization (PSO) algorithm, combined with a regression model, is used to dynamically adjust system parameters. This intelligent optimization approach ensures stable operation and maximizes digestion efficiency under varying environmental conditions. System testing under dynamic conditions demonstrates a sorting accuracy of 98% along with highly efficient biological conversion. The proposed framework offers a scalable, intelligent, and practical solution for modern waste management, making it suitable for both residential and industrial applications.

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