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

• URL: http://arxiv.org/abs/2604.14882v1
• Date: Thu, 16 Apr 2026 11:19:36 GMT
• Title: An Intelligent Robotic and Bio-Digestor Framework for Smart Waste Management
• Abstract summary: This paper presents the design and evaluation of an integrated waste management framework that combines a robotic waste segregation module and an optimized bio-digestor.<n>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.<n>The bio-digestor system is equipped with multiple sensors to monitor key parameters, including temperature, pH, pressure, and motor revolutions per minute.
• Score: 3.289878169836672
• License: http://creativecommons.org/licenses/by/4.0/
• 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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