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Cited by 204BIM and Construction IntegrationInnovations in Concrete and Construction MaterialsRobot Manipulation and Learning

Recent advancements of robotics in construction

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Xiao, Bo, Chen, Chen, Yin, Xianfei·2022·Automation in Construction
DOI 10.1016/j.autcon.2022.104591
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Abstract

In the past two decades, robotics in construction (RiC) has become an interdisciplinary research field that integrates a large number of urgent technologies (e.g., additive manufacturing, deep learning, building information modelling (BIM)), resulting in the related literature being both fragmented and vast. This paper has explored the advances in RiC in the past two decades using a mixed quantitative-qualitative review method. Initially, 940 related articles (170 journal articles and 770 conference papers) were identified by keyword-searching in Scopus and then fed into a bibliometric analysis to build science maps. Following this, a qualitative discussion highlights recent achievements in RiC across three dimensions: tasks, algorithms, and collaborations. Moreover, four future research directions are proposed: 1) in-depth integration of BIM and robotics; 2) near-site robotic fabrication; 3) deep reinforcement learning for flexible environment adaption; and 4) high-level robot-to-robot collaboration. The contributions of this research are twofold: 1) identifying the latest research topics and trends concerning robotic technologies in construction; and 2) providing in-depth insights into the future direction of RiC. The findings from this research can serve both academia and industry in terms of promoting robotic algorithms, hardware, and applications in construction industry.

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  • The low level of standardization and the dynamic nature of construction environments present significant challenges for robotic automation.

    “However, the low level of standardization and the dynamic nature of construction environments present significant challenges for robotic automation [2].”

    From Real-time 3D Perception System of Construction Equipment for Autonomous Mobile Robots (2025) · cites this paper as [2]

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  1. M. Golparvar-Fard, F. Peña-Mora, S. Savarese, Automated progress monitoring using unordered daily construction photographs and IFC-based building information models, Journal of Computing in Civil Engineering 29 (2015) 04014025, https://doi.org/10.1061/(ASCE)CP.1943-5487.0000205. · DOI
  2. K.S. Saidi, T. Bock, C. Georgoulas, Robotics in construction, in: Springer Handbook of Robotics, Springer International Publishing, Cham, 2016, pp. 1493-1520, https://doi.org/10.1007/978-3-319-32552-1_57. · DOI
  3. J.S. Albus, NBS/RIA Robotics Research Workshop: Proceedings of the NBS/RIA Workshop on Robotic Research, held at the National Bureau of Standards in Gaithersburg, MD, on November 13-15, 1979, U.S. Dept. of Commerce, Washington, D.C., 1981. ISBN-10:0331424320.
  4. C. Balaguer, A. Mohamed, Trends in robotics and automation in construction, in: Robotics and Automation in Construction, InTech, 2008, https://doi.org/ 10.5772/5865. · DOI
  5. Q.P. Ha, L. Yen, C. Balaguer, Robotic autonomous systems for earthmoving in military applications, Automation in Construction 107 (2019), 102934, https:// doi.org/10.1016/j.autcon.2019.102934. · DOI
  6. T. Tankova, L.S. da Silva, Robotics and Additive Manufacturing in the Construction Industry, Current Robotics Reports. 1 (2020) 13-18, https://doi. org/10.1007/s43154-020-00003-8. · DOI · Paper page
  7. M.Z. Abbas, K. Abu Baker, M. Ayaz, H. Mohamed, M. Tariq, A. Ahmed, M. Faheem, Key factors involved in pipeline monitoring techniques using robots and WSNs: comprehensive survey, Journal of Pipeline Systems Engineering and Practice 9 (2018) 04018001, https://doi.org/10.1061/(ASCE)PS.1949- 1204.0000305. · DOI
  8. D. Lattanzi, G. Miller, Review of robotic infrastructure inspection systems, Journal of Infrastructure Systems 23 (2017) 04017004, https://doi.org/10.1061/ (ASCE)IS.1943-555X.0000353. · DOI
  9. M. Pan, W. Pan, Determinants of adoption of robotics in precast concrete production for buildings, Journal of Management in Engineering 35 (2019) 05019007, https://doi.org/10.1061/(ASCE)ME.1943-5479.0000706. · DOI
  10. R. Jin, P.X.W. Zou, P. Piroozfar, H. Wood, Y. Yang, L. Yan, Y. Han, A science mapping approach based review of construction safety research, Safety Science 113 (2019) 285-297, https://doi.org/10.1016/j.ssci.2018.12.006. · DOI
  11. M. Oraee, M.R. Hosseini, E. Papadonikolaki, R. Palliyaguru, M. Arashpour, Collaboration in BIM-based construction networks: a bibliometric-qualitative literature review, International Journal of Project Management 35 (2017) 1288-1301, https://doi.org/10.1016/J.IJPROMAN.2017.07.001. · DOI
  12. X. Yin, H. Liu, Y. Chen, M. Al-Hussein, Building information modelling for off-site construction: review and future directions, Automation in Construction 101 (2019) 72-91, https://doi.org/10.1016/j.autcon.2019.01.010. · DOI
  13. R. Jin, P.X.W. Zou, P. Piroozfar, H. Wood, Y. Yang, L. Yan, Y. Han, A science mapping approach based review of construction safety research, Safety Science 113 (2019) 285-297, https://doi.org/10.1016/j.ssci.2018.12.006. · DOI
  14. P. Martinez, M. Al-Hussein, R. Ahmad, A scientometric analysis and critical review of computer vision applications for construction, Automation in Construction 107 (2019), https://doi.org/10.1016/j.autcon.2019.102947. · DOI
  15. M. Senthilvel, K. Varghese, N. Ramesh Babu, Building information modeling for precast construction: a review of research and practice, in: Construction Research Congress 2016: Old and New Construction Technologies Converge in Historic San Juan -Proceedings of the 2016 Construction Research Congress 2016, CRC, 2016, pp. 2250-2259, https://doi.org/10.1061/9780784479827.224. · DOI
  16. R. Jin, H. Yuan, Q. Chen, Science mapping approach to assisting the review of construction and demolition waste management research published between 2009 and 2018, Resources, Conservation and Recycling 140 (2019) 175-188, https://doi.org/10.1016/j.resconrec.2018.09.029. · DOI
  17. Y. Zhang, H. Liu, S.C. Kang, M. Al-Hussein, Virtual reality applications for the built environment: research trends and opportunities, Automation in Construction 118 (2020), 103311, https://doi.org/10.1016/j. autcon.2020.103311. · DOI
  18. M.R. Hosseini, I. Martek, E.K. Zavadskas, A.A. Aibinu, M. Arashpour, N. Chileshe, Critical evaluation of off-site construction research: a Scientometric analysis, Automation in Construction 87 (2018) 235-247, https://doi.org/10.1016/J. AUTCON.2017.12.002. · DOI
  19. P. Mongeon, A. Paul-Hus, The journal coverage of Web of Science and Scopus: a comparative analysis, Scientometrics. (2016), https://doi.org/10.1007/s11192- 015-1765-5. · DOI · Paper page
  20. M.R. Hosseini, I. Martek, E.K. Zavadskas, A.A. Aibinu, M. Arashpour, N. Chileshe, Critical evaluation of off-site construction research: a scientometric analysis, Automation in Construction 87 (2018) 235-247, https://doi.org/10.1016/J. AUTCON.2017.12.002. · DOI
  21. N.J. van Eck, L. Waltman, Software survey: VOSviewer, a computer program for bibliometric mapping, Scientometrics 84 (2010) 523-538, https://doi.org/ 10.1007/s11192-009-0146-3. · DOI · Paper page
  22. Y. Zhang, H. Liu, S.C. Kang, M. Al-Hussein, Virtual reality applications for the built environment: research trends and opportunities, Automation in Construction 118 (2020), 103311, https://doi.org/10.1016/j. autcon.2020.103311. · DOI
  23. N.J. van Eck, L. Waltman, Text mining and visualization using VOSviewer, ISSI Newsletter. 7 (2011) 50-54, https://doi.org/10.1371/journal.pone.0054847. · DOI
  24. N.J. van Eck, L. Waltman, Citation-based clustering of publications using CitNetExplorer and VOSviewer, Scientometrics 111 (2017) 1053-1070, https:// doi.org/10.1007/s11192-017-2300-7. · DOI
  25. H. Small, Co-citation in the scientific literature: a new measure of the relationship between two documents, Journal of the American Society for Information Science 24 (1973) 265-269, https://doi.org/10.1002/asi.4630240406. · DOI · Paper page
  26. X. Zhao, A scientometric review of global BIM research: analysis and visualization, Automation in Construction 80 (2017) 37-47, https://doi.org/ 10.1016/j.autcon.2017.04.002. · DOI
  27. H. Small, Co-citation in the scientific literature: a new measure of the relationship between two documents, Journal of the American Society for Information Science 24 (1973) 265-269, https://doi.org/10.1002/asi.4630240406. · DOI · Paper page
  28. T. Bock, The future of construction automation: technological disruption and the upcoming ubiquity of robotics, Automation in Construction 59 (2015) 113-121, https://doi.org/10.1016/j.autcon.2015.07.022. · DOI
  29. T.D. Oesterreich, F. Teuteberg, Understanding the implications of digitisation and automation in the context of Industry 4.0: a triangulation approach and elements of a research agenda for the construction industry, Computers in Industry 83 (2016) 121-139, https://doi.org/10.1016/j.compind.2016.09.006. · DOI
  30. J. Song, C.T. Haas, C. Caldas, E. Ergen, B. Akinci, Automating the task of tracking the delivery and receipt of fabricated pipe spools in industrial projects, Automation in Construction 15 (2006) 166-177, https://doi.org/10.1016/j. autcon.2005.03.001. · DOI
  31. A. Warszawski, R. Navon, Implementation of robotics in building: current status and future prospects, Journal of Construction Engineering and Management 124 (1998) 31-41, https://doi.org/10.1061/(ASCE)0733-9364(1998)124:1(31). · DOI
  32. K. Ulrich, The role of product architecture in the manufacturing firm, Research Policy 24 (1995) 419-440, https://doi.org/10.1016/0048-7333(94)00775-3. · DOI
  33. J. Werfel, K. Petersen, R. Nagpal, Designing collective behavior in a termite- inspired robot construction team, Science 343 (2014) (1979) 754-758, https:// doi.org/10.1126/science.1245842. · DOI · Paper page
  34. E.J. Jaselskis, M.R. Anderson, C.T. Jahren, Y. Rodriguez, S. Njos, Radio-frequency identification applications in construction industry, Journal of Construction Engineering and Management 121 (1995) 189-196, https://doi.org/10.1061/ (ASCE)0733-9364(1995)121:2(189). · DOI
  35. B. Khoshnevis, Automated construction by contour crafting-related robotics and information technologies, Automation in Construction 13 (2004) 5-19, https:// doi.org/10.1016/j.autcon.2003.08.012. · DOI
  36. N. Melenbrink, J. Werfel, A. Menges, On-site autonomous construction robots: Towards unsupervised building, Automation in Construction 119 (2020), 103312, https://doi.org/10.1016/j.autcon.2020.103312. · DOI
  37. S. Siebert, J. Teizer, Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system, Automation in Construction 41 (2014) 1-14, https://doi.org/10.1016/j.autcon.2014.01.004. · DOI
  38. L.-C. Wang, Enhancing construction quality inspection and management using RFID technology, Automation in Construction 17 (2008) 467-479, https://doi. org/10.1016/j.autcon.2007.08.005. · DOI
  39. X. Zhang, M. Li, J.H. Lim, Y. Weng, Y.W.D. Tay, H. Pham, Q.-C. Pham, Large-scale 3D printing by a team of mobile robots, Automation in Construction 95 (2018) 98-106, https://doi.org/10.1016/j.autcon.2018.08.004. · DOI
  40. N.J. van Eck, L. Waltman, Software survey: VOSviewer, a computer program for bibliometric mapping, Scientometrics. 84 (2010) 523-538, https://doi.org/ 10.1007/s11192-009-0146-3. · DOI · Paper page
  41. P.C. Lee, H.N.N. Su, Investigating the structure of regional innovation system research through keyword co-occurrence and social network analysis, Innovation: Management, Policy and Practice (2010), https://doi.org/10.5172/impp.12.1.26. · DOI
  42. G. Pritschow, M. Dalacker, J. Kurz, M. Gaenssle, Technological aspects in the development of a mobile bricklaying robot, Automation in Construction 5 (1996) 3-13, https://doi.org/10.1016/0926-5805(95)00015-1. · DOI
  43. K. Dörfler, T. Sandy, M. Giftthaler, F. Gramazio, M. Kohler, J. Buchli, Mobile robotic brickwork, in: Robotic Fabrication in Architecture, Art and Design 2016, Springer International Publishing, Cham, 2016, pp. 204-217, https://doi.org/ 10.1007/978-3-319-26378-6_15. · DOI
  44. B.A. Zandavali, M.J. García, Automated brick pattern generator for robotic assembly using machine learning and images, in: Blucher Design Proceedings, Editora Blucher, São Paulo, 2019, pp. 217-226, https://doi.org/10.5151/ proceedings-ecaadesigradi2019_605. · DOI
  45. L. Ding, W. Jiang, Y. Zhou, C. Zhou, S. Liu, BIM-based task-level planning for robotic brick assembly through image-based 3D modeling, Advanced Engineering Informatics 43 (2020), 100993, https://doi.org/10.1016/j.aei.2019.100993. · DOI · Paper page
  46. R.R. Murphy, E. Steimle, M. Hall, M. Lindemuth, D. Trejo, S. Hurlebaus, Z. Medina-Cetina, D. Slocum, Robot-assisted bridge inspection, Journal of Intelligent and Robotic Systems 64 (2011) 77-95, https://doi.org/10.1007/ s10846-010-9514-8. · DOI
  47. A.Q. Pham, H.M. La, K.T. La, M.T. Nguyen, A Magnetic Wheeled Robot for Steel Bridge Inspection, 2020, pp. 11-17, https://doi.org/10.1007/978-3-030-37497- 6_2. · DOI
  48. S.T. Nguyen, H.M. La, A climbing robot for steel bridge inspection, Journal of Intelligent and Robotic Systems 102 (2021) 75, https://doi.org/10.1007/s10846- 020-01266-1. · DOI
  49. N. Mir-Nasiri, H.S. J, M.H. Ali, Portable autonomous window cleaning robot, Procedia Computer Science. 133 (2018) 197-204, https://doi.org/10.1016/j. procs.2018.07.024. · DOI
  50. C. Walter, J. Saenz, N. Elkmann, H. Althoff, S. Kutzner, T. Stuerze, Design considerations of robotic system for cleaning and inspection of large-diameter sewers, Journal of Field Robotics 29 (2012) 186-214, https://doi.org/10.1002/ rob.20428. · DOI
  51. X. Li, S.H. Han, M. Gül, M. Al-Hussein, Automated post-3D visualization ergonomic analysis system for rapid workplace design in modular construction, Automation in Construction 98 (2019) 160-174, https://doi.org/10.1016/j. autcon.2018.11.012. · DOI
  52. Y. Yang, M. Pan, W. Pan, 'Co-evolution through interaction' of innovative building technologies: The case of modular integrated construction and robotics, Automation in Construction 107 (2019), 102932, https://doi.org/10.1016/j. autcon.2019.102932. · DOI
  53. A. Garg, V.R. Kamat, Virtual prototyping for robotic fabrication of rebar cages in manufactured concrete construction, Journal of Architectural Engineering 20 (2014) 06013002, https://doi.org/10.1061/(ASCE)AE.1943-5568.0000134. · DOI
  54. S. Reichenbach, B. Kromoser, State of practice of automation in precast concrete production, Journal of Building Engineering. 43 (2021), 102527, https://doi.org/ 10.1016/j.jobe.2021.102527. · DOI
  55. C.-J. Liang, S.-C. Kang, M.-H. Lee, RAS: a robotic assembly system for steel structure erection and assembly, International Journal of Intelligent Robotics and Applications. 1 (2017) 459-476, https://doi.org/10.1007/s41315-017-0030-x. · DOI
  56. S. An, P. Martinez, M. Al-Hussein, R. Ahmad, Automated verification of 3D manufacturability for steel frame assemblies, Automation in Construction 118 (2020), https://doi.org/10.1016/j.autcon.2020.103287. · DOI
  57. E.M. Villanueva, H. Mamledesai, P. Martinez, P. Poostchi, R. Ahmad, Design and simulation of an automated robotic machining cell for cross-laminated timber panels, Procedia CIRP. 100 (2021) 175-180, https://doi.org/10.1016/j. procir.2021.05.026. · DOI
  58. H. Hasan, A. Reddy, A. Tsayjacobs, Robotic Fabrication of Nail Laminated Timber, 2019, https://doi.org/10.22260/ISARC2019/0162. · DOI
  59. R. Naboni, A. Kunic, A. Kramberger, C. Schlette, Design, simulation and robotic assembly of reversible timber structures, Construction Robotics 5 (2021) 13-22, https://doi.org/10.1007/s41693-020-00052-7. · DOI
  60. A. Al Rashid, S.A. Khan, S.G. Al-Ghamdi, M. Koç, Additive manufacturing: technology, applications, markets, and opportunities for the built environment, Automation in Construction 118 (2020), 103268, https://doi.org/10.1016/j. autcon.2020.103268. · DOI
  61. T.T. Le, S.A. Austin, S. Lim, R.A. Buswell, A.G.F. Gibb, T. Thorpe, Mix design and fresh properties for high-performance printing concrete, Materials and Structures 45 (2012) 1221-1232, https://doi.org/10.1617/s11527-012-9828-z. · DOI
  62. P. Carneau, R. Mesnil, N. Roussel, O. Baverel, Additive manufacturing of cantilever -from masonry to concrete 3D printing, Automation in Construction 116 (2020), 103184, https://doi.org/10.1016/j.autcon.2020.103184. · DOI
  63. N. Ducoulombier, L. Demont, C. Chateau, M. Bornert, J.-F. Caron, Additive manufacturing of anisotropic concrete: A flow-based pultrusion of continuous fibers in a cementitious matrix, Procedia Manufacturing. 47 (2020) 1070-1077, https://doi.org/10.1016/j.promfg.2020.04.117. · DOI
  64. B. García de Soto, I. Agustí-Juan, J. Hunhevicz, S. Joss, K. Graser, G. Habert, B. T. Adey, Productivity of digital fabrication in construction: Cost and time analysis of a robotically built wall, Automation in Construction 92 (2018) 297-311, https://doi.org/10.1016/j.autcon.2018.04.004. · DOI
  65. C. Buchanan, L. Gardner, Metal 3D printing in construction: a review of methods, research, applications, opportunities and challenges, Engineering Structures 180 (2019) 332-348, https://doi.org/10.1016/j.engstruct.2018.11.045. · DOI
  66. D. Ding, Z. Pan, D. Cuiuri, H. Li, A multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM), Robotics and Computer-Integrated Manufacturing 31 (2015) 101-110, https://doi.org/10.1016/j.rcim.2014.08.008. · DOI
  67. L. Yuan, Z. Pan, D. Ding, Z. Yu, S. van Duin, H. Li, W. Li, J. Norrish, Fabrication of metallic parts with overhanging structures using the robotic wire arc additive manufacturing, Journal of Manufacturing Processes 63 (2021) 24-34, https:// doi.org/10.1016/j.jmapro.2020.03.018. · DOI
  68. P. Urhal, A. Weightman, C. Diver, P. Bartolo, Robot assisted additive manufacturing: A review, Robotics and Computer-Integrated Manufacturing 59 (2019) 335-345, https://doi.org/10.1016/j.rcim.2019.05.005. · DOI
  69. N. Travitzky, A. Bonet, B. Dermeik, T. Fey, I. Filbert-Demut, L. Schlier, T. Schlordt, P. Greil, Additive manufacturing of ceramic-based materials, Advanced Engineering Materials 16 (2014) 729-754, https://doi.org/10.1002/ adem.201400097. · DOI
  70. T. Li, L. Wang, Bending behavior of sandwich composite structures with tunable 3D-printed core materials, Composite Structures 175 (2017) 46-57, https://doi. org/10.1016/j.compstruct.2017.05.001. · DOI
  71. N. Pereira da Silva, S. Eloy, Robotic Construction: Robotic Fabrication Experiments for the Building Construction Industry, 2021, pp. 97-109, https:// doi.org/10.1007/978-3-030-35533-3_14. · DOI
  72. Z. Wang, H. Li, X. Zhang, Construction waste recycling robot for nails and screws: Computer vision technology and neural network approach, Automation in Construction 97 (2019) 220-228, https://doi.org/10.1016/j. autcon.2018.11.009. · DOI
  73. K. Asadi, H. Ramshankar, H. Pullagurla, A. Bhandare, S. Shanbhag, P. Mehta, S. Kundu, K. Han, E. Lobaton, T. Wu, Vision-based integrated mobile robotic system for real-time applications in construction, Automation in Construction 96 (2018) 470-482, https://doi.org/10.1016/j.autcon.2018.10.009. · DOI
  74. J.J. Lin, A. Ibrahim, S. Sarwade, M. Golparvar-Fard, Bridge inspection with aerial robots: automating the entire pipeline of visual data capture, 3D mapping, defect detection, analysis, and reporting, Journal of Computing in Civil Engineering 35 (2021) 04020064, https://doi.org/10.1061/(asce)cp.1943-5487.0000954. · DOI
  75. E. Valero, A. Adán, Integration of RFID with other technologies in construction, Measurement. 94 (2016) 614-620, https://doi.org/10.1016/j. measurement.2016.08.037. · DOI
  76. D. Won, S. Chi, M.-W. Park, UAV-RFID integration for construction resource localization, KSCE Journal of Civil Engineering 24 (2020) 1683-1695, https:// doi.org/10.1007/s12205-020-2074-y. · DOI
  77. A. Luque, A. Carrasco, A. Martín, A., de las Heras, The impact of class imbalance in classification performance metrics based on the binary confusion matrix, Pattern Recognition 91 (2019) 216-231, https://doi.org/10.1016/j. patcog.2019.02.023. · DOI
  78. P. Vestartas, Y. Weinand, Laser scanning with industrial robot arm for raw-wood fabrication, in: Proceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020: From Demonstration to Practical Use - To New Stage of Construction Robot, 2020, pp. 773-780, https://doi.org/ 10.22260/ISARC2020/0107. · DOI
  79. E. Valero, A. Adán, F. Bosché, Semantic 3D reconstruction of furnished interiors using laser scanning and RFID technology, Journal of Computing in Civil Engineering 30 (2016) 04015053, https://doi.org/10.1061/(ASCE)CP.1943- 5487.0000525. · DOI
  80. Y. Fu, C. Wang, R. Liu, G. Liang, H. Zhang, S. Ur Rehman, Moving object localization based on UHF RFID phase and laser clustering, Sensors. 18 (2018) 825, https://doi.org/10.3390/s18030825. · DOI
  81. W. Fu, R. Liu, H. Wang, R. Ali, Y. He, Z. Cao, Z. Qin, A method of multiple dynamic objects identification and localization based on laser and RFID, Sensors. 20 (2020) 3948, https://doi.org/10.3390/s20143948. · DOI
  82. P.-Y. Lajoie, B. Ramtoula, Y. Chang, L. Carlone, G. Beltrame, DOOR-SLAM: Distributed, online, and outlier resilient SLAM for robotic teams, IEEE Robotics and Automation Letters. 5 (2020) 1656-1663, https://doi.org/10.1109/ LRA.2020.2967681. · DOI
  83. E. McLaughlin, N. Charron, S. Narasimhan, Automated defect quantification in concrete bridges using robotics and deep learning, Journal of Computing in Civil Engineering 34 (2020) 04020029, https://doi.org/10.1061/(asce)cp.1943- 5487.0000915. · DOI
  84. J. Zhang, S. Singh, Low-drift and real-time lidar odometry and mapping, Autonomous Robots 41 (2017) 401-416, https://doi.org/10.1007/s10514-016- 9548-2. · DOI
  85. A. Gunatilake, M. Galea, K. Thiyagarajan, S. Kodagoda, L. Piyathilaka, P. Darji, Using UHF-RFID signals for robot localization inside pipelines, in: 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), IEEE, 2021, pp. 1109-1114, https://doi.org/10.1109/ICIEA51954.2021.9516284. · DOI
  86. B.K. Patle, G. Babu, A. Pandey, D.R.K. Parhi, A. Jagadeesh, A review: on path planning strategies for navigation of mobile robot, Defence, Technology 15 (2019) 582-606, https://doi.org/10.1016/j.dt.2019.04.011. · DOI
  87. K. Asadi, V.R. Haritsa, K. Han, J.-P. Ore, Automated object manipulation using vision-based mobile robotic system for construction applications, Journal of Computing in Civil Engineering 35 (2021) 04020058, https://doi.org/10.1061/ (asce)cp.1943-5487.0000946. · DOI
  88. B.R.K. Mantha, C.C. Menassa, V.R. Kamat, Task allocation and route planning for robotic service networks in indoor building environments, Journal of Computing in Civil Engineering 31 (2017) 04017038, https://doi.org/10.1061/(asce) cp.1943-5487.0000687. · DOI
  89. X. Wang, C.-J. Liang, C.C. Menassa, V.R. Kamat, Interactive and immersive process-level digital twin for collaborative human-robot construction work, Journal of Computing in Civil Engineering 35 (2021) 04021023, https://doi.org/ 10.1061/(asce)cp.1943-5487.0000988. · DOI
  90. C.-H. Yang, S.-C. Kang, Collision avoidance method for robotic modular home prefabrication, Automation in Construction 130 (2021), 103853, https://doi.org/ 10.1016/j.autcon.2021.103853. · DOI
  91. P. Chotiprayanakul, D.K. Liu, G. Dissanayake, Human-robot-environment interaction interface for robotic grit-blasting of complex steel bridges, Automation in Construction 27 (2012) 11-23, https://doi.org/10.1016/j. autcon.2012.04.014. · DOI
  92. S. Kim, M. Peavy, P.-C. Huang, K. Kim, Development of BIM-integrated construction robot task planning and simulation system, Automation in Construction 127 (2021), 103720, https://doi.org/10.1016/j. autcon.2021.103720. · DOI
  93. D. Karimi, S. Braga, R. Gomes, Ivanka Iordanova, St-Onge, Semantic optimal robot navigation using building information on construction sites, in: 2021 ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, Waterloo, Canada, 2021, pp. 57-64, https://doi.org/10.22260/ isarc2021/0010. · DOI
  94. J. Park, Y.K. Cho, D. Martinez, A BIM and UWB integrated mobile robot navigation system for indoor position tracking applications, Journal of Construction Engineering and Project Management. 6 (2016) 30-39, https://doi. org/10.6106/JCEPM.2016.6.2.030. · DOI
  95. H. Yamamoto, Y. Ishimatsu, S. Ageishi, N. Ikeda, K. Endo, M. Masuda, M. Uchida, H. Yamaguchi, Example of Experimental Use of 3D Measurement System for Construction Robot Based on Component Design Concept, 2006, https://doi.org/ 10.22260/ISARC2006/0049. · DOI
  96. Q.H. Le, J.W. Lee, S.Y. Yang, Remote control of excavator using head tracking and flexible monitoring method, Automation in Construction 81 (2017) 99-111, https://doi.org/10.1016/j.autcon.2017.06.015. · DOI
  97. Q.P. Ha, L. Yen, C. Balaguer, Robotic autonomous systems for earthmoving in military applications, Automation in Construction 107 (2019), https://doi.org/ 10.1016/j.autcon.2019.102934. · DOI
  98. M. Kurien, M.K. Kim, M. Kopsida, I. Brilakis, Real-time simulation of construction workers using combined human body and hand tracking for robotic construction worker system, Automation in Construction 86 (2018) 125-137, https://doi.org/ 10.1016/j.autcon.2017.11.005. · DOI
  99. S.L. Kim, H.J. Suk, J.H. Kang, J.M. Jung, T.H. Laine, J. Westlin, Using Unity, 3D to facilitate mobile augmented reality game development, in: 2014 IEEE World Forum on Internet of Things (WF-IoT), IEEE, 2014, pp. 21-26, https://doi.org/ 10.1109/WF-IoT.2014.6803110. · DOI
  100. S. You, J.H. Kim, S.H. Lee, V. Kamat, L.P. Robert, Enhancing perceived safety in human-robot collaborative construction using immersive virtual environments, Automation in Construction 96 (2018) 161-170, https://doi.org/10.1016/j. autcon.2018.09.008. · DOI
  101. W. Turek, K. Cetnarowicz, A. Borkowski, On human-centric and robot-centric perspective of a building model, Automation in Construction 81 (2017) 2-16, https://doi.org/10.1016/j.autcon.2017.03.018. · DOI
  102. A. Quattrini Li, P.K. Penumarthi, J. Banfi, N. Basilico, J.M. O'Kane, I. Rekleitis, S. Nelakuditi, F. Amigoni, Multi-robot online sensing strategies for the construction of communication maps, Autonomous Robots 44 (2020) 299-319, https://doi.org/10.1007/s10514-019-09862-3. · DOI
  103. J. Fink, A. Ribeiro, V. Kumar, Robust control of mobility and communications in autonomous robot teams, IEEE Access. 1 (2013) 290-309, https://doi.org/ 10.1109/ACCESS.2013.2262013. · DOI
  104. H.J. Im, C.E. Lee, Y.J. Cho, Radio mapping scheme using collective intelligent robots for teleoperation in unstructured environments, in: IEEE RO-MAN 2014 IEEE International Symposium on Robot and Human Interactive Communication: Human-Robot Co-Existence: Adaptive Interfaces and Systems for Daily Life, Therapy, Assistance and Socially Engaging Interactions, 2014, pp. 856-861, https://doi.org/10.1109/ROMAN.2014.6926360. · DOI
  105. H. Miura, A. Watanabe, S. Suzuki, M. Okugawa, Field experiment report for tunnel disaster by investigation system with multiple robots, in: 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 2016, pp. 276-277, https://doi.org/10.1109/SSRR.2016.7784313. · DOI
  106. K. Nagatani, M. Abe, K. Osuka, P. Jo Chun, T. Okatani, M. Nishio, S. Chikushi, T. Matsubara, Y. Ikemoto, H. Asama, Innovative technologies for infrastructure construction and maintenance through collaborative robots based on an open design approach, Advanced Robotics 35 (2021) 715-722, https://doi.org/ 10.1080/01691864.2021.1929471. · DOI
  107. D. Wallace, Y.H. He, J. Chagas Vaz, L. Georgescu, P.Y. Oh, Multimodal teleoperation of heterogeneous robots within a construction environment, in: IEEE International Conference on Intelligent Robots and Systems, Institute of Electrical and Electronics Engineers Inc, 2020, pp. 2698-2705, https://doi.org/ 10.1109/IROS45743.2020.9340688. · DOI
  108. X. Zhao, A scientometric review of global BIM research: analysis and visualization, Automation in Construction 80 (2017) 37-47, https://doi.org/ 10.1016/j.autcon.2017.04.002. · DOI
  109. A. Hamieh, A. Ben Makhlouf, B. Louhichi, D. Deneux, A BIM-based method to plan indoor paths, Automation in Construction 113 (2020), 103120, https://doi. org/10.1016/j.autcon.2020.103120. · DOI
  110. C. Follini, V. Magnago, K. Freitag, M. Terzer, C. Marcher, M. Riedl, A. Giusti, D. T. Matt, BIM-integrated collaborative robotics for application in building construction and maintenance, Robotics 10 (2020) 2, https://doi.org/10.3390/ robotics10010002. · DOI · Paper page
  111. C.-H. Yang, T.-H. Wu, B. Xiao, S.-C. Kang, Design of a robotic software package for modular home builder, in (2019), https://doi.org/10.22260/ISARC2019/0163. · DOI
  112. S. Bodea, C. Zechmeister, N. Dambrosio, M. Dörstelmann, A. Menges, Robotic coreless filament winding for hyperboloid tubular composite components in construction, Automation in Construction 126 (2021), 103649, https://doi.org/ 10.1016/j.autcon.2021.103649. · DOI
  113. S. Gusmao Brissi, O. Wong Chong, L. Debs, J. Zhang, A review on the interactions of robotic systems and lean principles in offsite construction, Engineering Construction and Architectural Management 29 (2022) 383-406, https://doi.org/ 10.1108/ECAM-10-2020-0809. · DOI
  114. L. Hou, Y. Tan, W. Luo, S. Xu, C. Mao, S. Moon, Towards a more extensive application of off-site construction: a technological review, International Journal of Construction Management (2020) 1-12, https://doi.org/10.1080/ 15623599.2020.1768463. · DOI
  115. H.J. Wagner, M. Alvarez, O. Kyjanek, Z. Bhiri, M. Buck, A. Menges, Flexible and transportable robotic timber construction platform -TIM, Automation in Construction 120 (2020), 103400, https://doi.org/10.1016/j. autcon.2020.103400. · DOI · Paper page
  116. K. Arulkumaran, M.P. Deisenroth, M. Brundage, A.A. Bharath, Deep reinforcement learning: a brief survey, IEEE Signal Processing Magazine 34 (2017) 26-38, https://doi.org/10.1109/MSP.2017.2743240. · DOI · Paper page
  117. A. Heuillet, F. Couthouis, N. Díaz-Rodríguez, Explainability in deep reinforcement learning, Knowledge-Based Systems 214 (2021), 106685, https://doi.org/ 10.1016/j.knosys.2020.106685. · DOI
  118. Q. Huang, R. Huang, W. Hao, J. Tan, R. Fan, Z. Huang, Adaptive power system emergency control using deep reinforcement learning, IEEE Transactions on Smart Grid 11 (2020) 1171-1182, https://doi.org/10.1109/TSG.2019.2933191. · DOI

Cited by

Sorted by citations

Authors
Xiao, B., Chen, C., & Yin, X.
Affiliations
  • Hong Kong Polytechnic University (HK)
  • Zhejiang University of Science and Technology (CN)
  • University of Twente (NL)
Corresponding author
Yin, Xianfei
ORCID
Venue
Automation in Construction
Publisher
Elsevier BV
Published
2022-12
Volume
144
Pages
104591
ISSN
0926-5805 (ISSN-L)
DOI
10.1016/j.autcon.2022.104591
OpenAlex
W4297985168
Cited by
204
References
3
Citations by year
2026: 60 · 2025: 65 · 2024: 48 · 2023: 28 · 2022: 3
FWCI
15.74
Type
journal article
Language
en
Open access
Licence
Locations
  • Automation in Construction (publishedVersion, cc-by-nc-nd)
  • University of Twente Research Information (publishedVersion, cc-by-nc-nd)
  • University of Twente Research Information (submittedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Engineering › Building and Construction › BIM and Construction Integration
  • Physical Sciences › Engineering › Building and Construction › Innovations in Concrete and Construction Materials
  • Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
Keywords
construction robotics, construction automation, building information modeling, robotic fabrication, additive manufacturing, deep reinforcement learning, science mapping, research trends

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