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.

翻译：本文提出了一种新颖的方法，将可信人工智能（AI）与节能型机械臂相结合，用于智能垃圾分类与分拣。该系统通过利用基于MobileNetV2进行迁移学习增强的卷积神经网络（CNN），将垃圾准确分类为六个类别：塑料、玻璃、金属、纸张、纸板和一般垃圾。该模型实现了99.8%的高训练准确率和80.5%的验证准确率，表现出强大的学习与泛化能力。研究实现了一个机械臂模拟器进行虚拟分拣，并使用欧几里得距离计算每个动作的能耗，以确保运动的最优化与高效性。该框架整合了可信AI的关键要素，如透明度、鲁棒性、公平性和安全性，使其成为适用于城市智能垃圾管理系统的可靠且可扩展的解决方案。