Optimizing Waste Sorting for Sustainability: An AI-Powered Robotic Solution for Beverage Container Recycling

Tianhao Cheng, Daiki Kojima, Hao Hu, Hiroshi Onoda and Andante Hadi Pandyaswargo ()
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Tianhao Cheng: Graduate School of Environment and Energy Engineering, Waseda University, Shinjuku-ku, Tokyo 162-0041, Japan
Daiki Kojima: Graduate School of Environment and Energy Engineering, Waseda University, Shinjuku-ku, Tokyo 162-0041, Japan
Hao Hu: EII, Inc., Chiyoda-ku, Tokyo 101-0054, Japan
Hiroshi Onoda: Graduate School of Environment and Energy Engineering, Waseda University, Shinjuku-ku, Tokyo 162-0041, Japan
Andante Hadi Pandyaswargo: Environmental Research Institute, Waseda University, Shinjuku-ku, Tokyo 162-0041, Japan

Sustainability, 2024, vol. 16, issue 23, 1-18

Abstract: With Japan facing workforce shortages and the need for enhanced recycling systems due to an aging population and increasing environmental challenges, automation in recycling facilities has become a key component for advancing sustainability goals. This study presents the development of an automated sorting robot to replace manual processes in beverage container recycling, aiming to address environmental, social, and economic sustainability by optimizing resource efficiency and reducing labor demands. Using artificial intelligence (AI) for image recognition and high-speed suction-based grippers, the robot effectively sorts various container types, including PET bottles and clear and colored glass bottles, demonstrating a pathway toward more sustainable waste management practices. The findings indicate that stabilizing items on the sorting line may enhance acquisition success, although clear container detection remains an AI challenge. This research supports the United Nation’s 2030 Agenda for Sustainable Development by advancing recycling technology to improve waste processing efficiency, thus contributing to reduced pollution, resource conservation, and a sustainable recycling infrastructure. Further development of gripper designs to handle deformed or liquid-containing containers is required to enhance the system’s overall sustainability impact in the recycling sector.

Keywords: gripper development; image recognition; labor sustainability; recycling automation; resource efficiency (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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