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Mapping the Evolution of the Robotics Industry: A cross country comparison

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Abstract

Industry 4.0 may be regarded as an emerging approach to the adoption of next-generation robotics for industrial applications. Our study sheds light on the current state of robotics, with a particular focus on robots for industrial applications. The research combines publicly-available information from company press releases, news articles, peer-reviewed journals and trade and industry reports. The paper is organized in four sections. Section 1 discusses some definitions of robotics and robotics subclasses, and various robotics classifications. Sections 2 and 3 provide a snapshot of demand and supply of robotics, and offers some insights into select regional markets and global technological trends. Section 4 describes the challenges and opportunities surrounding robotics and Industry 4.0, and the future impact of these technologies.

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  • Estolatan, Eric & Geuna, Aldo & Guerzoni, Marco & Massimiliano Nuccio,, 2018. "Mapping the Evolution of the Robotics Industry: A cross country comparison," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201812, University of Turin.
    • Handle: RePEc:uto:dipeco:201812
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    1. Andrew Keisner & Julio Raffo & Sacha Wunsch-Vincent, 2015. "Breakthrough technologies - Robotics, innovation and intellectual property," WIPO Economic Research Working Papers 30, World Intellectual Property Organization - Economics and Statistics Division.
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    Cited by:

    1. Massimiliano Nuccio & Marco Guerzoni & Riccardo Cappelli & Aldo Geuna, 2020. "Industrial pattern and robot adoption in European regions," Working Papers 03, Department of Management, Università Ca' Foscari Venezia.
    2. Klump, Rainer & Jurkat, Anne & Schneider, Florian, 2021. "Tracking the rise of robots: A survey of the IFR database and its applications," MPRA Paper 110390, University Library of Munich, Germany.
    3. Marco Guerzoni & Consuelo R. Nava & Massimiliano Nuccio, 2019. "The survival of start-ups in time of crisis. A machine learning approach to measure innovation," Papers 1911.01073, arXiv.org.

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