Kathea V. Bolivar, Kryzl V. Deceo, Yvan Caesar M. De Guzman, Mary Glynes A. Galilea, Deanddra F. Robles, Louie Jay L.Sombito, Rosalyn S. Garde
College of Engineering, University of Saint La Salle, La Salle Avenue, Bacolod City, Negros Occidental, Philippines
Abstract
Rapid urbanization and population growth have significantly increased the demand for efficient waste management systems. This study presents an autonomous garbage disposal and profiling system with smart scheduling aimed at improving waste collection efficiency and data-driven decision-making. The proposed system consists of an autonomous dual-compartment garbage bin and a stationary main receptacle, enabling the segregation and profiling of biodegradable and non-biodegradable waste. The autonomous bin navigates predefined routes and disposes collected waste based on height thresholds, scheduled intervals, or manual override. System status, including bin capacity and battery level, is monitored through a mobile application, while waste profiling data are stored on a web server accessible to local authorities. Smart scheduling is implemented using machine learning, where disposal frequency data are analyzed to predict when the main receptacle will reach its capacity threshold. Results demonstrate the feasibility of integrating autonomous collection, waste profiling, and predictive scheduling into a unified smart waste management system suitable for controlled urban environments.
Keywords: arduino, data management, raspberry pi, trash