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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2510.17408 |
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| _version_ | 1866917027533291520 |
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| author | Kure, Halima I. Retnakumari, Jishna Nwajana, Augustine O. Ismail, Umar M. Romo, Bilyaminu A. Egho-Promise, Ehigiator |
| author_facet | Kure, Halima I. Retnakumari, Jishna Nwajana, Augustine O. Ismail, Umar M. Romo, Bilyaminu A. Egho-Promise, Ehigiator |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17408 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integrating Trustworthy Artificial Intelligence with Energy-Efficient Robotic Arms for Waste Sorting Kure, Halima I. Retnakumari, Jishna Nwajana, Augustine O. Ismail, Umar M. Romo, Bilyaminu A. Egho-Promise, Ehigiator Robotics Systems and Control 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. |
| title | Integrating Trustworthy Artificial Intelligence with Energy-Efficient Robotic Arms for Waste Sorting |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2510.17408 |