用可信AI+节能机械臂实现六类垃圾精准分拣
Integrating Trustworthy Artificial Intelligence with Energy-Efficient Robotic Arms for Waste Sorting
- 用迁移学习增强的MobileNetV2模型分类垃圾
- 验证准确率达80.5%，训练准确率99.8%
- 兼顾透明性、鲁棒性，适合城市智能环卫
本文提出一种新型方法，将可信人工智能（AI）与节能型机械臂结合，用于智能垃圾分类与分拣。通过使用基于MobileNetV2的迁移学习增强卷积神经网络（CNN），系统可将垃圾准确分为六类：塑料、玻璃、金属、纸张、纸板和其他垃圾。模型训练准确率达99.8%，验证准确率为80.5%，展现出强学习能力和良好泛化性能。采用机械臂仿真器进行虚拟分拣，利用欧氏距离计算每项动作的能耗，确保运动路径最优高效。该框架融合了可信AI的关键要素，包括透明性、鲁棒性、公平性和安全性，为城市环境中的智能垃圾管理系统提供了可靠且可扩展的解决方案。
原文摘要 · Abstract (English)
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.
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