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Robots present an innovative solution to the construction industry’s challenges, including safety concerns, skilled worker shortages, and productivity issues. Successfully collaborating with robots requires new competencies to ensure safety, smooth interaction, and accelerated adoption of robotic technologies. However, limited research exists on the specific competencies needed for human—robot collaboration in construction. Moreover, the perspectives of construction industry professionals on these competencies remain underexplored. This study examines the perceptions of construction industry professionals regarding the knowledge, skills, and abilities necessary for the effective implementation of human—robot collaboration in construction. A two-round Delphi survey was conducted with expert panel members from the construction industry to assess their views on the competencies for human—robot collaboration. The results reveal that the most critical competencies include knowledge areas such as human—robot interface, construction robot applications, human—robot collaboration safety and standards, task planning and robot control system; skills such as task planning, safety management, technical expertise, human—robot interface, and communication; and abilities such as safety awareness, continuous learning, problemsolving, critical thinking, and spatial awareness. This study contributes to knowledge by identifying the most significant competencies for human—robot collaboration in construction and highlighting their relative importance. These competencies could inform the design of educational and training programs and facilitate the integration of robotic technologies in construction. The findings also provide a foundation for future research to further explore and enhance these competencies, ultimately supporting safer, more efficient, and more productive construction practices.
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This content will become publicly available on May 14, 2026
A Modular Quadruped Control and Sensor Integration Framework with a Case Study in Worker Activity Recognition
Quadrupeds are becoming increasingly popular in construction engineering research and practice for their affordability and accessibility. These robots navigate uneven terrain commonly found in construction sites, making them suitable vehicles for sensors and monitoring tasks. However, the lack of streamlined and fully developed client-side software packages inhibits rapid deployment of application-specific models to the field. Furthermore, substantial prerequisite knowledge of computer science and programming significantly impedes the ability of non-experts to adapt the robots to specific applications. In this work, we present a comprehensive framework to address these gaps in accessibility, enabling users to customize these robots to their needs. This framework provides a template that facilitates seamless communication between the robotic vehicle, edge devices, sensors, pathfinding algorithms, and a Unity simulation for mission planning and execution. As an example of this framework’s flexibility, we have conducted a case study using this template to demonstrate an application of the framework in the construction domain that performs worker activity recognition and features a novel self-labeling mechanism for construction activity video data. The findings highlight the potential of accessible software tools in expanding the utility of robotic platforms across various engineering domains.
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- Award ID(s):
- 2047138
- PAR ID:
- 10617089
- Publisher / Repository:
- American Society of Civil Engineers
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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