Artificial Intelligence in Human–Robot Collaboration in the Construction Industry: A Scoping Review

Abstract

With the gradual rise of automation and human–robot collaboration (HRC), artificial intelligence (AI) is expected to significantly change the construction industry by automating design and decision-making processes, thus improving both productivity and safety. Despite the growing research trends in AI and HRC, no study has synthesized the existing studies of AI in HRC in the construction industry. This paper aims to conduct a review of AI in HRC in construction and summarize the current mainstream topics, research gaps, and future research directions. A scoping review and science mapping analysis were used to explore extant literature in the studied domain and conduct keyword co-occurrence analysis, respectively. In this study, 210 relevant articles were retrieved from the Scopus database from 1993 to July 2025. The results revealed five main clusters regarding the co-occurrence of keywords. Four mainstream research topics were discussed, including (1) AI techniques and applications, (2) the use of extended reality (XR) in HRC, (3) the challenges of HRC, and (4) the application of HRC in the architecture, engineering, and construction (AEC) sector. Moreover, this study provided a detailed summary of research gaps and future research directions. These findings offer researchers and practitioners a deeper understanding of AI applications in HRC for construction case studies and serve as actionable directions to advance this field.

Publication DOI: https://doi.org/10.3390/buildings15173060
Divisions: College of Engineering & Physical Sciences
Aston University (General)
Additional Information: Copyright © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Publication ISSN: 2075-5309
Last Modified: 02 Sep 2025 11:09
Date Deposited: 02 Sep 2025 11:09
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Related URLs: https://www.mdp ... 5309/15/17/3060 (Publisher URL)
PURE Output Type: Article
Published Date: 2025-09
Published Online Date: 2025-08-27
Accepted Date: 2025-08-24
Authors: Peng, Bo
Antwi-Afari, Maxwell Fordjour (ORCID Profile 0000-0002-6812-7839)
Manzoor, Bilal
Boateng, Evans
Afari, Emmanuel Nyamekye Antwi
Wu, Zezhou

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