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.
Abstract (translated)
本文提出了一种新颖的方法，该方法将值得信赖的人工智能（AI）与节能机器人手臂相结合，用于智能垃圾分类和分拣。通过利用基于MobileNetV2的迁移学习增强的卷积神经网络（CNN），系统能够准确地将垃圾分类为六类：塑料、玻璃、金属、纸张、硬纸板和其他垃圾。该模型在训练阶段达到了99.8%的高精度，在验证阶段则达到80.5%的准确性，显示出了强大的学习能力和泛化能力。 为了模拟虚拟分拣过程，设计了一个机器人手臂仿真器，通过计算每个动作的欧几里得距离来估算能耗，确保操作既高效又节能。该框架集成了值得信赖的人工智能的关键要素，包括透明性、鲁棒性、公平性和安全性，使其成为城市环境中智能垃圾分类系统可靠且可扩展的解决方案。
URL
https://arxiv.org/abs/2510.17408
