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Review | Open Access

Intelligent robots and human–robot collaboration in the construction industry: A review

Hsi-Hien Wei1Yuting Zhang2Ximing Sun2Jiayu Chen2( )Shixian Li1
Department of Building & Real Estate, The Hong Kong Polytechnic University, Hong Kong, China
School of Civil Engineering, Tsinghua University, Beijing 100084, China
Show Author Information


The construction industry is a typical labor-intensive industry, which suffers from low productivity and labor shortage in the past decades. Recently, the developments in robotics and artificial intelligence technologies highlight the evolutionary reforming potential in the construction industry. An increasing number of robots are joining construction tasks and collaborating with human workers. This study reviews the major developments in intelligent robots and human–robot collaboration (HRC) in the construction industry. The technological foundations and fundamental concepts of construction robots and collaborative robots are reviewed, organized, and discussed to reveal that progress has been made. Based on a comprehensive review, the major challenges and future research directions of HRC have been proposed and examined. This study finally developed a comprehensive and in-depth discussion of the state-of-the-art implementation of robotics technologies in the construction industry and shed light on its path to future development.


M. J. Ribririnho, J. Mischke, G. Strube, et al. The next normal in construction: How disruption is reshaping the world’s largest ecosystem [Online]. (accessed 2020-06-04).

E. Gambao, C. Balaguer, F. Gebhart. Robot assembly system for computer-integrated construction. Automat Constr, 2000, 9: 479–487.


S. H. Ghaffar, J. Corker, M. Z. Fan. Additive manufacturing technology and its implementation in construction as an eco-innovative solution. Automat Constr, 2018, 93: 1–11.


N. King, M. Bechthold, A. Kane, et al. Robotic tile placement: Tools, techniques and feasibility. Automat Constr, 2014, 39: 161–166.

M. Vujović, A. Rodić, I. Stevanović. Design of modular re-configurable robotic system for construction and digital fabrication. In: Advances in Robot Design and Intelligent Control. A. Rodić, T. Borangiu, Eds. Cham (Germany): Springer, 2017: pp 550–559.

C. J. Liang, X. Wang, V. R. Kamat, et al. Human–robot collaboration in construction: Classification and research trends. J Constr Eng M, 2021, 147: 03121006.


V. Arndt, D. Rothenbacher, U. Daniel, et al. Construction work and risk of occupational disability: A ten year follow up of 14,474 male workers. Occup Environ Med, 2005, 62: 559–566.

V. S. S. Kumar, I. Prasanthi, A. Leena. Robotics and automation in construction industry. In: Proceedings of the Architectural Engineering Conference (AEI) 2008, Denver, USA, 2008: pp 1–9.

M. Laborde, V. Sanvido. Introducing new process technologies into construction companies. J Constr Eng M, 1994, 120: 488–508.

J. Seagers, Y. Z. Liu, H. Jebelli. Smart robotic system to fight the spread of COVID-19 at construction sites. In: Proceedings of the Construction Research Congress 2022, Arlington, USA, 2022: pp 452–461.

X. Y. Ma, C. Mao, G. W. Liu. Can robots replace human beings?—Assessment on the developmental potential of construction robot. J Build Eng, 2022, 56: 104727.


C. Feng, Y. Xiao, A. Willette, et al. Vision guided autonomous robotic assembly and as-built scanning on unstructured construction sites. Automat Constr, 2015, 59: 128–138.

K. S. Saidi, T. Bock, C. Georgoulas. Robotics in construction. In: Springer Handbook of Robotics. B. Siciliano, O. Khatib, Eds. Cham (Germany): Springer, 2016: pp 1493–1520.

S. You, J. H. Kim, S. Lee, et al. Enhancing perceived safety in human–robot collaborative construction using immersive virtual environments. Automat Constr, 2018, 96: 161–170.


A. Ajoudani, A. M. Zanchettin, S. Ivaldi, et al. Progress and prospects of the human–robot collaboration. Auton Robot, 2018, 42: 957–975.


P. Vähä, T. Heikkilä, P. Kilpeläinen, et al. Extending automation of building construction—Survey on potential sensor technologies and robotic applications. Automat Constr, 2013, 36: 168–178.


P. K. Panigrahi, S. K. Bisoy. Localization strategies for autonomous mobile robots: A review. J King Saud Univ—Com, 2022, 34: 6019–6039.

W. Burgard, A. Derr, D. Fox, et al. Integrating global position estimation and position tracking for mobile robots: The dynamic Markov localization approach. In: Proceedings of 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications, Victoria, Canada, 1998: pp 730–735.
C. Rohrig, F. Kunemund. Mobile robot localization using WLAN signal strengths. In: Proceedings of the 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Dortmund, Germany, 2007: pp 704–709.
L. Zhang, R. Zapata, P. Lépinay. Self-adaptive Monte Carlo localization for mobile robots using range sensors. In: Proceedings of 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, USA, 2009: pp 1541–1546.

F. Andrade, M. Llofriu, M. M. Tanco, et al. Active localization strategy for hypotheses pruning in challenging environments. J Intell Robot Syst, 2022, 106: 47.


J. Borenstein, L. Q. Feng. Measurement and correction of systematic odometry errors in mobile robots. IEEE T Robotic Autom, 1996, 12: 869–880.

S. I. Roumeliotis, G. A. Bekey. Bayesian estimation and Kalman filtering: A unified framework for mobile robot localization. In: Proceedings of 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings, San Francisco, USA, 2000: pp 2985–2992.

T. J. Chong, X. J. Tang, C. H. Leng, et al. Sensor technologies and simultaneous localization and mapping (SLAM). Procedia Comput Sci, 2015, 76: 174–179.


Y. Sun, J. Hu, J. T. Yun, et al. Multi-objective location and mapping based on deep learning and visual slam. Sensors, 2022, 22: 7576.

D. Hahnel, W. Burgard, D. Fox, et al. Mapping and localization with RFID technology. In: Proceedings of the IEEE International Conference on Robotics and Automation, 2004, New Orleans, USA, 2004: pp 1015–1020.

W. Gueaieb, S. Miah. An intelligent mobile robot navigation technique using RFID technology. IEEE T Instrum Meas, 2008, 57: 1908–1917.

P. K. Panigrahi, H. K. Tripathy. Analysis on intelligent based navigation and path finding of autonomous mobile robot. In: Information Systems Design and Intelligent Applications. J. K. Mandal, S. C. Satapathy, M. K. Sanyal, et al, Eds. New Delhi (India): Springer, 2015: pp 219–232.
S. Halder, K. Afsari, J. Serdakowski, et al. Accuracy estimation for autonomous navigation of a quadruped robot in construction progress monitoring. In: Proceedings of the ASCE International Conference on Computing in Civil Engineering 2021, Orlando, USA, 2022: pp 1092–1100.

B. Ekanayake, J. K. W. Wong, A. A. F. Fini, et al. Computer vision-based interior construction progress monitoring: A literature review and future research directions. Automat Constr, 2021, 127: 103705.


B. Jiang, A. Mamishev. Robotic monitoring of power systems. IEEE T Power Delivery, 2004, 19: 912–918.


S. Sony, S. Laventure, A. Sadhu. A literature review of next-generation smart sensing technology in structural health monitoring. Struct Control Health Monit, 2019, 26: e2321.


K. Asadi, H. Ramshankar, H. Pullagurla, et al. Vision-based integrated mobile robotic system for real-time applications in construction. Automat Constr, 2018, 96: 470–482.


C. Y. Zhang, D. Arditi. Automated progress control using laser scanning technology. Automat Constr, 2013, 36: 108–116.

J. H. Lee, J. H. Park, B. T. Jang. Design of robot based work progress monitoring system for the building construction site. In: Proceedings of 2018 International Conference on Information and Communication Technology Convergence, Jeju, Republic of Korea, 2018: pp 1420–1422.

K. C. Yeh, M. H. Tsai, S. C. Kang. On-site building information retrieval by using projection-based augmented reality. J Comput Civil Eng, 2012, 26: 342–355.


M. Kopsida, I. Brilakis. Real-time volume-to-plane comparison for mixed reality-based progress monitoring. J Comput Civil Eng, 2020, 34: 04020016.


T. Bock, D. Stricker, J. Fliedner, et al. Automatic generation of the controlling-system for a wall construction robot. Automat Constr, 1996, 5: 15–21.


Y. Z. Liu, M. Habibnezhad, H. Jebelli. Brain–computer interface for hands-free teleoperation of construction robots. Automat Constr, 2021, 123: 103523.

A. Gawel, H. Blum, J. Pankert, et al. A fully-integrated sensing and control system for high-accuracy mobile robotic building construction. In: Proceedings of 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, Macau, China, 2019: pp 2300–2307.

T. Brogårdh. Present and future robot control development—An industrial perspective. Annu Rev Control, 2007, 31: 69–79.


Y. S. Zhao, L. Gong, Y. X. Huang, et al. A review of key techniques of vision-based control for harvesting robot. Comput Electron Agr, 2016, 127: 311–323.

E. Gambao, M. Hernando. Control system for a semi-automatic façade cleaning robot. In: Proceedings of the 23rd ISARC, Tokyo, Japan, 2006: pp 406–411.

H. L. Wang, W. Ren, C. C. Cheah, et al. Dynamic modularity approach to adaptive control of robotic systems with closed architecture. IEEE T Automat Contr, 2020, 65: 2760–2767.


C. Follini, V. Magnago, K. Freitag, et al. BIM-integrated collaborative robotics for application in building construction and maintenance. Robotics, 2021, 10: 2.


L. Y. Ding, Y. Zhou, B. Akinci. Building information modeling (BIM) application framework: The process of expanding from 3D to computable nD. Automat Constr, 2014, 46: 82–93.


O. W. Chong, J. S. Zhang, R. M. Voyles, et al. BIM-based simulation of construction robotics in the assembly process of wood frames. Automat Constr, 2022, 137: 104194.


L. Y. Ding, W. G. Jiang, Y. Zhou, et al. BIM-based task-level planning for robotic brick assembly through image-based 3D modeling. Adv Eng Inform, 2020, 43: 100993.


O. Davtalab, A. Kazemian, B. Khoshnevis. Perspectives on a BIM-integrated software platform for robotic construction through contour crafting. Automat Constr, 2018, 89: 13–23.


L. Monostori. Cyber–physical production systems: Roots, expectations and R&D challenges. Proc CIRP, 2014, 17: 9–13.


M. N. O. Sadiku, Y. H. Wang, S. X. Cui, et al. Cyber–physical systems: A literature review. Eur Sci J, 2017, 13: 52.


Y. Ashibani, Q. H. Mahmoud. Cyber physical systems security: Analysis, challenges and solutions. Comput Secur, 2017, 68: 81–97.

P. Jain, P. K. Aggarwal, P. Chaudhary, et al. Convergence of IoT and CPS in robotics. In: Emergence of Cyber Physical System and IoT in Smart Automation and Robotics. K. K. Singh, A. Nayyar, S. Tanwar, et al, Eds. Cham (Germany): Springer, 2021: pp 15–30.

Q. M. Tan, Y. F. Tong, S. F. Wu, et al. Modeling, planning, and scheduling of shop–floor assembly process with dynamic cyber–physical interactions: A case study for CPS-based smart industrial robot production. Int J Adv Manuf Technol, 2019, 105: 3979–3989.


N. Nikolakis, V. Maratos, S. Makris. A cyber physical system (CPS) approach for safe human–robot collaboration in a shared workplace. Robot Cim-Int Manuf, 2019, 56: 233–243.


D. L. Goodhue, R. L. Thompson. Task–technology fit and individual performance. MIS Quart, 1995, 19: 213–236.


H. Gao, Q. Li, G. Lv. Green management analysis of construction projects based on full life-cycle. Adv Mat Res, 2013, 689: 13–17.


D. K. Ahadzie, D. G. Proverbs, P. Olomolaiye. Towards developing competency-based measures for construction project managers: Should contextual behaviours be distinguished from task behaviours? Int J Proj Manag, 2008, 26: 631–645.


A. V. Dunaevsky, V. V. Peshkov. Flow-line production method in the residential construction: Analysis of the state, problems and development trends. IOP Conf Ser: Earth Environ Sci, 2021, 751: 012073.


B. Xiao, C. Chen, X. F. Yin. Recent advancements of robotics in construction. Automat Constr, 2022, 144: 104591.


T. T. Le, S. A. Austin, S. Lim, et al. Mix design and fresh properties for high-performance printing concrete. Mater Struct, 2012, 45: 1221–1232.


S. Woo, D. Hong, W. C. Lee, et al. A robotic system for road lane painting. Automat Constr, 2008, 17: 122–129.

M. Gautam, H. Fagerlund, B. Greicevci, et al. Collaborative robotics in construction: A test case on screwing gypsum boards on ceiling. In: Proceedings of the 5th International Conference on Green Technology and Sustainable Development, Ho Chi Minh City, Vietnam, 2020: pp 88–93.

X. Wang, C. J. Liang, C. C. Menassa, et al. Interactive and immersive process-level digital twin for collaborative human–robot construction work. J Comput Civil Eng, 2021, 35: 04021023.

K. Jung, B. Chu, K. Bae, et al. Development of automation system for steel construction based on robotic crane. In: Proceedings of 2008 International Conference on Smart Manufacturing Application, Goyangi, Republic of Korea, 2008: pp 486–489.

L. Wang, T. Zhang, H. Fukuda, et al. Research on the application of mobile robot in timber structure architecture. Sustainability, 2022, 14: 4681.

V. Karnowski, A. S. Kümpel. Diffusion of innovations. In: Schlüsselwerke der Medienwirkungsforschung. M. Potthoff, Ed. Wiesbaden (Germany): Springer, 2016: pp 97–107. (in German)

S. Lee, J. Yu. Comparative study of BIM acceptance between Korea and the United States. J Constr Eng M, 2016, 142: 05015016.


J. Whyte, D. Bouchlaghem, T. Thorpe. IT implementation in the construction organization. Eng Constr Archit Manag, 2002, 9: 371.


R. Drazin. The processes of technological innovation. J Technol Transfer, 1991, 16: 45–46.


M. Pan, W. Pan. Understanding the determinants of construction robot adoption: Perspective of building contractors. J Constr Eng M, 2020, 146: 04020040.


S. Lim, R. A. Buswell, T. T. Le, et al. Developments in construction-scale additive manufacturing processes. Automat Constr, 2012, 21: 262–268.


R. Bogue. What are the prospects for robots in the construction industry? Ind Robot, 2018, 45: 1–6.


M. Pan, T. Linner, W. Pan, et al. A framework of indicators for assessing construction automation and robotics in the sustainability context. J Clean Prod, 2018, 182: 82–95.


H. F. Lin. Understanding the determinants of electronic supply chain management system adoption: Using the technology–organization–environment framework. Technol Forecast Soc, 2014, 86: 80–92.


F. Thiesse, T. Staake, P. Schmitt, et al. The rise of the “next-generation bar code”: An international RFID adoption study. Supply Chain Manag, 2011, 16: 328–345.

The Society of Automotive Engineers. Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles J3016_202104 [Online]. (accessed 2021-04-30).

J. M. Beer, A. D. Fisk, W. A. Rogers. Toward a framework for levels of robot autonomy in human–robot interaction. J Hum–Robot Interact, 2014, 3: 74–99.

K. S. Saidi, T. Bock, C. Georgoulas. Robotics in construction BT. In: Springer Handbook of Robotics. B. Siciliano, O. Khatib, Eds. Cham (Germany): Springer, 2016: pp 1493–1520.

C. J. Liang, V. R. Kamat, C. C. Menassa. Teaching robots to perform quasi-repetitive construction tasks through human demonstration. Automat Constr, 2020, 120: 103370.


R. Parasuraman, T. B. Sheridan, C. D. Wickens. A model for types and levels of human interaction with automation. IEEE T Syst Man Cy A, 2000, 30: 286–297.


J. K. Wang, W. N. Chen, X. Xiao, et al. A survey of the development of biomimetic intelligence and robotics. BIROB, 2021, 1: 100001.


S. Kadam, V. Vaidya. Cognitive evaluation of machine learning agents. Cogn Syst Res, 2021, 66: 100–121.


N. Balfe, S. Sharples, J. R. Wilson. Impact of automation: Measurement of performance, workload and behaviour in a complex control environment. Appl Ergon, 2015, 47: 52–64.


Z. Abuwarda, K. Mostafa, A. Oetomo, et al. Wearable devices: Cross benefits from healthcare to construction. Automat Constr, 2022, 142: 104501.


J. P. Shu, W. H. Li, Y. F. Gao. Collision-free trajectory planning for robotic assembly of lightweight structures. Automat Constr, 2022, 142: 104520.

J. Martinez, M. J. Black, J. Romero. On human motion prediction using recurrent neural networks. In: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: pp 4674–4683.

G. Guerra-Filho, A. Biswas. The human motion database: A cognitive and parametric sampling of human motion. Image Vision Comput, 2012, 30: 251–261.


X. L. Xia, T. Y. Zhou, J. Du, et al. Human motion prediction for intelligent construction: A review. Automat Constr, 2022, 142: 104497.


Z. A. Al-Sabbag, C. M. Yeum, S. Narasimhan. Enabling human–machine collaboration in infrastructure inspections through mixed reality. Adv Eng Inform, 2022, 53: 101709.


Y. C. Lee, M. Shariatfar, A. Rashidi, et al. Evidence-driven sound detection for prenotification and identification of construction safety hazards and accidents. Automat Constr, 2020, 113: 103127.


Y. Z. Liu, M. Habibnezhad, H. Jebelli. Brainwave-driven human–robot collaboration in construction. Automat Constr, 2021, 124: 103556.

Y. Z. Liu, H. Jebelli. Human–robot co-adaptation in construction: Bio-signal based control of bricklaying robots. In: Proceedings of the ASCE International Conference on Computing in Civil Engineering 2021, Orlando, USA, 2022: pp 304–312.

J. Kim, S. Chi, C. R. Ahn. Hybrid kinematic–visual sensing approach for activity recognition of construction equipment. J Build Eng, 2021, 44: 102709.


D. A. Linares-Garcia, N. Roofigari-Esfahan, K. Pratt, et al. Voice-based intelligent virtual agents (VIVA) to support construction worker productivity. Automat Constr, 2022, 143: 104554.


P. Adami, P. B. Rodrigues, P. J. Woods, et al. Impact of VR-based training on human–robot interaction for remote operating construction robots. J Comput Civil Eng, 2022, 36: 04022006.


X. Ma, Q. L. Qi, J. F. Cheng, et al. A consistency method for digital twin model of human–robot collaboration. J Manuf Syst, 2022, 65: 550–563.


C. Firth, K. Dunn, M. H. Haeusler, et al. Anthropomorphic soft robotic end-effector for use with collaborative robots in the construction industry. Automat Constr, 2022, 138: 104218.

S. Marcheschi, F. Salsedo, M. Fontana, et al. Body extender: Whole body exoskeleton for human power augmentation. In: Proceedings of 2011 IEEE International Conference on Robotics and Automation, Shanghai, China, 2011: pp 611–616.

Z. H. Zhu, A. Dutta, F. Dai. Exoskeletons for manual material handling—A review and implication for construction applications. Automat Constr, 2021, 122: 103493.


M. F. Antwi-Afari, H. Li, S. Anwer, et al. Assessment of a passive exoskeleton system on spinal biomechanics and subjective responses during manual repetitive handling tasks among construction workers. Safety Sci, 2021, 142: 105382.

J. Babič, K. Mombaur, D. Lefeber, et al. SPEXOR: Spinal exoskeletal robot for low back pain prevention and vocational reintegration BT. In: Wearable Robotics: Challenges and Trends. J. González-Vargas, J. Ibáñez, J. L. Contreras-Vidal, Eds. Cham (Germany): Springer, 2017: pp 311–315.
H. Kawamoto, S. Taal, H. Niniss, et al. Voluntary motion support control of robot suit HAL triggered by bioelectrical signal for hemiplegia. In: Proceedings of 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina, 2010: pp 462–466.
H. Kazerooni, J. L. Racine, L. H. Huang, et al. On the control of the Berkeley Lower Extremity Exoskeleton (BLEEX). In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain, 2005: pp 4353–4360.

H. Yu, I. S. Choi, K. L. Han, et al. Development of a upper-limb exoskeleton robot for refractory construction. Control Eng Pract, 2018, 72: 104–113.


B. Ren, X. R. Luo, H. Li, et al. Gait trajectory-based interactive controller for lower limb exoskeletons for construction workers. Comput Aided Civ Inf, 2022, 37: 558–572.

Y. K. Cho, K. Kim, S. Ma, et al. A robotic wearable exoskeleton for construction worker’s safety and health. In: Proceedings of the Construction Research Congress 2018, New Orleans, USA, 2018: pp 19–28.

J. M. D. Delgado, L. Oyedele, A. Ajayi, et al. Robotics and automated systems in construction: Understanding industry-specific challenges for adoption. J Build Eng, 2019, 26: 100868.


T. Bock. The future of construction automation: Technological disruption and the upcoming ubiquity of robotics. Automat Constr, 2015, 59: 113–121.

S. Lee, J. Moon II. Introduction of human–robot cooperation technologyat construction sites. In: Proceedings of the 31st ISARC, Sydney, Australia, 2014: pp 978–983.

C. J. Liang, K. M. Lundeen, W. McGee, et al. A vision-based marker-less pose estimation system for articulated construction robots. Automat Constr, 2019, 104: 80–94.


D. Kim, S. Lee, V. R. Kamat. Proximity prediction of mobile objects to prevent contact-driven accidents in co-robotic construction. J Comput Civil Eng, 2020, 34: 04020022.


M. Y. Zhang, S. Chen, X. F. Zhao, et al. Research on construction workers’ activity recognition based on smartphone. Sensors, 2018, 18: 2667.


W. W. Wu, H. J. Yang, D. A. S. Chew, et al. Towards an autonomous real-time tracking system of near-miss accidents on construction sites. Automat Constr, 2010, 19: 134–141.

T. Bock, T. Linner. Robot-Oriented Design. Cambridge (USA): Cambridge University Press, 2015.
C. D. Wickens, W. S. Helton, J. G. Hollands, et al. Engineering Psychology and Human Performance. New York (USA): Routledge, 2021.

F. R. Hamzeh, H. Alhussein, F. Faek. Investigating the practice of improvisation in construction. J Manage Eng, 2018, 34: 04018039.


K. Arulkumaran, M. P. Deisenroth, M. Brundage, et al. Deep reinforcement learning: A brief survey. IEEE Signal Proc Mag, 2017, 34: 26–38.


J. Ibarz, J. Tan, C. Finn, et al. How to train your robot with deep reinforcement learning: Lessons we have learned. Int J Robot Res, 2021, 40: 698–721.


A. Q. Li, P. K. Penumarthi, J. Banfi, et al. Multi-robot online sensing strategies for the construction of communication maps. Auton Rob, 2020, 44: 299–319.

H. Im, C. E. Lee, Y. J. Cho. Radio mapping scheme using collective intelligent robots for teleoperation in unstructured environments. In: Proceedings of the 23rd IEEE International Symposium on Robot and Human Interactive Communication, Edinburgh, UK, 2014: pp 856–861.
H. Miura, A. Watanabe, S. Suzuki, et al. Field experiment report for tunnel disaster by investigation system with multiple robots. In: Proceedings of 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics, Lausanne, Switzerland, 2016: pp 276–277.

A. Hamieh, A. Ben Makhlouf, B. Louhichi, et al. A BIM-based method to plan indoor paths. Automat Constr, 2020, 113: 103120.


S. Kim, M. Peavy, P. C. Huang, et al. Development of BIM-integrated construction robot task planning and simulation system. Automat Constr, 2021, 127: 103720.

C. H. Yang, T. H. Wu, B. Xiao, et al. Design of a robotic software package for modular home builder. In: Proceedings of the 36th ISARC, Banff, Canada, 2019: pp 1217–1222.

L. Hou, Y. T. Tan, W. K. Luo, et al. Towards a more extensive application of off-site construction: A technological review. Int J Constr Manag, 2022, 22: 2154–2165.


H. J. Wagner, M. Alvarez, O. Kyjanek, et al. Flexible and transportable robotic timber construction platform—TIM. Automat Constr, 2020, 120: 103400.

Journal of Intelligent Construction
Article number: 9180002
Cite this article:
Wei H-H, Zhang Y, Sun X, et al. Intelligent robots and human–robot collaboration in the construction industry: A review. Journal of Intelligent Construction, 2023, 1(1): 9180002.








Received: 21 December 2022
Revised: 11 February 2023
Accepted: 20 February 2023
Published: 29 March 2023
© The Author(s) 2023. Published by Tsinghua University Press.

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