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Implementation and Performance Analysis of an Industrial Robot’s Vision System Based on Cloud Vision Services

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  • Ioana-Livia Stefan

    (Automatic Control and Systems Engineering Department, Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica of Bucharest, Splaiul Independentei, No. 313, 060042 Bucharest, Romania
    These authors contributed equally to this work.)

  • Andrei Mateescu

    (Automatic Control and Systems Engineering Department, Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica of Bucharest, Splaiul Independentei, No. 313, 060042 Bucharest, Romania
    These authors contributed equally to this work.)

  • Ionut Lentoiu

    (Automatics and Industrial Informatics Department, Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica of Bucharest, Splaiul Independentei, No. 313, 060042 Bucharest, Romania)

  • Silviu Raileanu

    (Automatics and Industrial Informatics Department, Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica of Bucharest, Splaiul Independentei, No. 313, 060042 Bucharest, Romania)

  • Florin Daniel Anton

    (Automatics and Industrial Informatics Department, Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica of Bucharest, Splaiul Independentei, No. 313, 060042 Bucharest, Romania)

  • Dragos Constantin Popescu

    (Automatic Control and Systems Engineering Department, Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica of Bucharest, Splaiul Independentei, No. 313, 060042 Bucharest, Romania)

  • Ioan Stefan Sacala

    (Automatic Control and Systems Engineering Department, Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica of Bucharest, Splaiul Independentei, No. 313, 060042 Bucharest, Romania)

Abstract

With its fast advancements, cloud computing opens many opportunities for research in various applications from the robotics field. In our paper, we further explore the prospect of integrating Cloud AI object recognition services into an industrial robotics sorting task. Starting from our previously implemented solution on a digital twin, we are now putting our proposed architecture to the test in the real world, on an industrial robot, where factors such as illumination, shadows, different colors, and textures of the materials influence the performance of the vision system. We compare the results of our suggested method with those from an industrial machine vision software, indicating promising performance and opening additional application perspectives in the robotics field, simultaneously with the continuous improvement of Cloud and AI technology.

Suggested Citation

  • Ioana-Livia Stefan & Andrei Mateescu & Ionut Lentoiu & Silviu Raileanu & Florin Daniel Anton & Dragos Constantin Popescu & Ioan Stefan Sacala, 2025. "Implementation and Performance Analysis of an Industrial Robot’s Vision System Based on Cloud Vision Services," Future Internet, MDPI, vol. 17(5), pages 1-17, April.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:5:p:200-:d:1646460
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    References listed on IDEAS

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    1. Italo Cesidio Fantozzi & Annalisa Santolamazza & Giancarlo Loy & Massimiliano Maria Schiraldi, 2025. "Digital Twins: Strategic Guide to Utilize Digital Twins to Improve Operational Efficiency in Industry 4.0," Future Internet, MDPI, vol. 17(1), pages 1-16, January.
    2. Débora Souza & Gabriele Iwashima & Viviane Cunha Farias da Costa & Carlos Eduardo Barbosa & Jano Moreira de Souza & Geraldo Zimbrão, 2024. "Architectural Trends in Collaborative Computing: Approaches in the Internet of Everything Era," Future Internet, MDPI, vol. 16(12), pages 1-28, November.
    3. Maria Trigka & Elias Dritsas, 2025. "Edge and Cloud Computing in Smart Cities," Future Internet, MDPI, vol. 17(3), pages 1-41, March.
    4. Hadeel Amjed Saeed & Sufyan T. Faraj Al-Janabi & Esam Taha Yassen & Omar A. Aldhaibani, 2025. "Survey on Secure Scientific Workflow Scheduling in Cloud Environments," Future Internet, MDPI, vol. 17(2), pages 1-29, January.
    5. Salwa Sahnoun & Mahdi Mnif & Bilel Ghoul & Mohamed Jemal & Ahmed Fakhfakh & Olfa Kanoun, 2025. "Hybrid Solution Through Systematic Electrical Impedance Tomography Data Reduction and CNN Compression for Efficient Hand Gesture Recognition on Resource-Constrained IoT Devices," Future Internet, MDPI, vol. 17(2), pages 1-20, February.
    6. Ivaylo Atanasov & Dragomira Dimitrova & Evelina Pencheva & Ventsislav Trifonov, 2025. "Railway Cloud Resource Management as a Service," Future Internet, MDPI, vol. 17(5), pages 1-30, April.
    7. Konstantinos I. Roumeliotis & Nikolaos D. Tselikas & Dimitrios K. Nasiopoulos, 2025. "Fake News Detection and Classification: A Comparative Study of Convolutional Neural Networks, Large Language Models, and Natural Language Processing Models," Future Internet, MDPI, vol. 17(1), pages 1-29, January.
    8. Hesham Kamal & Maggie Mashaly, 2024. "Advanced Hybrid Transformer-CNN Deep Learning Model for Effective Intrusion Detection Systems with Class Imbalance Mitigation Using Resampling Techniques," Future Internet, MDPI, vol. 16(12), pages 1-74, December.
    9. Jerry Chou & Wu-Chun Chung, 2024. "Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet," Future Internet, MDPI, vol. 16(12), pages 1-4, December.
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