Field
Innovation and Entrepreneurship
Date
April 2026
Abstract
The global waste crisis is accelerating faster than current manual collection methods can manage, leading to significant environmental degradation and inefficient resource allocation. This system is designed to modernize waste collection through integrated robotics, computer vision, and real-time data tracking, providing a scalable solution for facility managers to identify and manage disposal trends effectively.
The core of the AUTO-BOT system is a mobile platform equipped with a high-precision robotic arm. While the project initially explored wide-scale autonomous navigation, the current focus has been refined to the critical tasks of object detection, classification, and physical collection. The robot utilizes a Raspberry Pi 5 as its primary processing unit, integrated with a high-definition camera and a potential 4-Degree of Freedom (4-DOF) robotic arm. To achieve high accuracy in cluttered environments, we implemented a custom-trained YOLOv8 (You Only Look Once) deep learning model. This model was trained using the TACO (Trash Annotations in Context) dataset, which we specifically refined to improve the detection of common litter items such as bottles, cans, and wrappers under varied lighting conditions.
Technically, the project addresses significant hardware challenges, including power management for multiple high-torque servos and the synchronization of dual Raspberry Pi units to balance processing loads. By isolating power supplies, the team prevented system reboots during intense motor actuation, ensuring continuous operation. AUTO-BOT represents a shift toward smart waste management, offering a reliable, data-centric alternative to manual labor. By automating the identification and retrieval of litter, this project demonstrates the potential for robotics to reduce the human footprint on the environment while providing the analytical tools necessary to foster long-term sustainability and recycling initiatives within large facilities and public spaces.
Recommended Citation
Badial, Savneet; Collantes, Michael; and Dinh, Minh, "Autonomous Utility for Trash Organization Robot (AUTO-BOT)" (2026). Pacific Innovation and Entrepreneurship Summit (PIES). 43.
https://scholarlycommons.pacific.edu/pies/43
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Tiger Tradeshow Competition - Special Track Winner: AI-Enabled Solutions