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

arXiv cs.RO / 4/17/2026

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Key Points

  • The paper proposes an integrated smart waste management framework that links real-time robotic waste segregation with an optimized bio-digestion system.
  • A MyCobot 280 robotic arm with a Jetson Nano platform uses YOLOv8 object detection and ROS-based path planning to classify and sort waste into four categories with high accuracy.
  • The separated biodegradable fraction is sent to a sensor-equipped bio-digestor that monitors temperature, pH, pressure, and motor RPM continuously.
  • Particle Swarm Optimization (PSO) combined with a regression model dynamically tunes the digestor parameters to maintain stable operation and maximize digestion efficiency under changing conditions.
  • Testing under dynamic conditions reports 98% sorting accuracy and highly efficient biological conversion, suggesting the approach is scalable for residential and industrial use.

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.