airo 23(1):

Research Article

Machine Learning in Robotics with Fog/Cloud Computing and IoT

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  • @ARTICLE{10.4108/airo.3621,
        author={Kiran Deep Singh and Prabh Deep Singh},
        title={Machine Learning in Robotics with Fog/Cloud Computing and IoT},
        journal={EAI Endorsed Transactions on AI and Robotics},
        volume={2},
        number={1},
        publisher={EAI},
        journal_a={AIRO},
        year={2023},
        month={12},
        keywords={Machine Learning, Robotics, Cloud Computing, Fog Computing, Internet of Things},
        doi={10.4108/airo.3621}
    }
    
  • Kiran Deep Singh
    Prabh Deep Singh
    Year: 2023
    Machine Learning in Robotics with Fog/Cloud Computing and IoT
    AIRO
    EAI
    DOI: 10.4108/airo.3621
Kiran Deep Singh1, Prabh Deep Singh2,*
  • 1: Chitkara University
  • 2: Graphic Era University
*Contact email: ssingh.prabhdeep@gmail.com

Abstract

Robotics has been transformed by machine learning (ML), enabling intelligent and adaptive autonomous systems. By delivering massive computational resources and real-time data, fog/cloud computing  and the Internet of Things boost ML-based robotics. Intelligent and linked robotics have emerged from fog/cloud computing, IoT, and machine learning. Robots using distributed computing, real-time IoT data, and advanced machine learning algorithms could alter industries and improve automation. To maximize its potential, this revolutionary combination must overcome several obstacles.  This paper discusses the benefits and drawbacks of integrating technologies. It offer rapid model training and deployment for robots ML algorithms like deep learning and reinforcement learning. Case studies demonstrate how this combination might enhance robotics across industries. This study discusses the benefits and drawbacks of fog/cloud computing, IoT, and machine learning in robots. We propose solutions for security and privacy, resource management, latency and bandwidth, interoperability, energy efficiency, data quality, and bias. By proactively addressing these difficulties, we can establish a secure, efficient, and privacy-conscious robotic ecosystem where robots seamlessly interact with the physical world, improving productivity, safety, and human-robot collaboration. As these technologies progress, appropriate integration and ethical principles are needed to maximize their benefits to society.