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Genealogy of Construction Robotics (2024-07)

10.1016/j.autcon.2024.105607

Wang Tingpeng, Mao Chao, Sun Bing, Li Zhiqiang
Journal Article - Automation in Construction, Vol. 166, No. 105607

Abstract

Construction robots, as a collection of multidisciplinary technologies, have been extensively studied and implemented by scholars and engineers to boost construction efficiency and minimize the reliance on manual labor. However, the practical application of construction robots has been limited due to a lack of systematic research. The paper explores the future research direction of construction robots using a systematic literature review and a genealogical approach. Moreover, this paper qualitatively discusses the systematic classification of construction robots in terms of “timeline”, “life cycle”, and “functional features”, forming three categories of construction robot genealogy. Moreover, future research directions are proposed from six perspectives: labor force structure, task division model, economic benefits, project management approach, research on the adaptability of construction robots to the site environment, and integration development. This paper provides new insights into the classification of construction robots and gives theoretical references for the subsequent development of construction robots.

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0 Citations

BibTeX
@article{wang_mao_sun_li.2024.GoCR,
  author            = "Tingpeng Wang and Chao Mao and Bing Sun and Zhiqiang Li",
  title             = "Genealogy of Construction Robotics",
  doi               = "10.1016/j.autcon.2024.105607",
  year              = "2024",
  journal           = "Automation in Construction",
  volume            = "166",
  pages             = "105607",
}
Formatted Citation

T. Wang, C. Mao, B. Sun and Z. Li, “Genealogy of Construction Robotics”, Automation in Construction, vol. 166, p. 105607, 2024, doi: 10.1016/j.autcon.2024.105607.

Wang, Tingpeng, Chao Mao, Bing Sun, and Zhiqiang Li. “Genealogy of Construction Robotics”. Automation in Construction 166 (2024): 105607. https://doi.org/10.1016/j.autcon.2024.105607.