Kure, Halima I., Retnakumari, Jishna, Nwajana, Augustine O. ORCID: https://orcid.org/0000-0001-6591-5269, Ismail, Umar M., Romo, Bilyaminu A. and Egho-Promise, Ehigiator (2025) Integrating trustworthy Artificial Intelligence with energy-efficient robotic arms for waste sorting. In: Proceedings of the 13th International Conference on Control, Mechatronics and Automation (ICCMA 2025) 24th - 26th November 2025, Paris, France. Institute of Electrical and Electronics Engineers, Inc. (IEEE). (In Press)

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Abstract

This paper presents a novel methodology that integrates trustworthy artificial intelligence (AI) with an energy-efficient robotic arm for intelligent waste classification and sorting. By utilizing a convolutional neural network (CNN) enhanced through transfer learning with MobileNetV2, the system accurately classifies waste into six categories: plastic, glass, metal, paper, cardboard, and trash. The model achieved a high training accuracy of 99.8% and a validation accuracy of 80.5%, demonstrating strong learning and generalization. A robotic arm simulator is implemented to perform virtual sorting, calculating the energy cost for each action using Euclidean distance to ensure optimal and efficient movement. The framework incorporates key elements of trustworthy AI, such as transparency, robustness, fairness, and safety, making it a reliable and scalable solution for smart waste management systems in urban settings.

Item Type:	Conference Proceedings
Title of Proceedings:	Proceedings of the 13th International Conference on Control, Mechatronics and Automation (ICCMA 2025) 24th - 26th November 2025, Paris, France
Additional Information:	Preprint: https://arxiv.org/abs/2510.17408
Uncontrolled Keywords:	Trustworthy AI, waste sorting, robotic arm, energy-efficient systems, CNN, transfer learning, MobileNetV2
Subjects:	Q Science > Q Science (General) T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	05 Nov 2025 09:10
URI:	https://gala.gre.ac.uk/id/eprint/51461

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