A scientometric and bibliometric analysis of three decades of global robotics and Internet of Things research trends and intellectual structure

The convergence of robotics and the Internet of Things (IoT) has transformed multiple sectors through autonomous, interconnected systems. Despite significant scholarly output, the knowledge structure of Robotics-IoT research remains fragmented. This study provides an integrated scientometric and bibliometric mapping of global Robotics–IoT/IoRT research, combining performance analysis, collaboration-network mapping, thematic mapping, and LDA-based topic modelling to reveal the field’s intellectual structure, collaboration architecture, dominant clusters, emerging themes, and future research frontiers. A scientometric analysis was conducted on 21,733 unique documents retrieved from Scopus and Web of Science after screening and deduplication of 23,377 initial records, covering 1993–2025 across 2,284 sources and over 44,000 indexed author-name records/clusters, cleaned via the PRISMA protocol. Biblioshiny and VOSviewer were employed for publication trends, citation structures, and collaborative networks, while LDA was applied for topic modelling. Robotics-IoT research shows rapid growth post-2016. Using all-author affiliation-country document-level full counting, India, China, and the USA lead publication output. Among the ten most productive countries, Australia shows the highest average citations per document, followed by Canada, the USA, and the United Kingdom. Collaboration networks reflect extensive global partnerships, with emerging economies demonstrating substantial international participation. Thematic analysis identifies major research streams related to industrial digitalization, IoT–robotic systems, energy-efficient networking, machine-learning-based analytics, robotics and sensing, cybersecurity, and edge/cloud computing, alongside specialized applications in agriculture and healthcare. Observed distributions show varying degrees of descriptive alignment with classical scientometric laws (Lotka’s, Bradford’s, Price’s, Zipf’s, Matthew Effect, and Pareto Principle). The originality of this study lies in the domain-specific application and empirical synthesis of these approaches to the global Robotics–IoT/IoRT research landscape. Specifically, the study provides a longitudinal analysis covering 1993–2025, examines the intellectual, social, publication and thematic structures of the Robotics–IoT/IoRT domain, evaluates a comparatively broad set of classical scientometric regularities, and complements keyword-based analysis with abstract-based LDA topic modelling. The study consequently contributes new empirical evidence concerning the evolution, concentration, collaboration, intellectual structure and thematic development of Robotics–IoT/IoRT research.