TY - GEN
T1 - Mapping Essential Competencies for Human-Robot Collaboration in Construction
T2 - 54th IEEE Frontiers in Education Conference, FIE 2024
AU - Olukanni, Ebenezer
AU - Akanmu, Abiola
AU - Afolabi, Adedeji
AU - Jebelli, Houtan
N1 - This work was supported by the National Science Foundation [grant numbers 2235375 and 2402008].
PY - 2024
Y1 - 2024
N2 - This research-to-practice full paper identifies the essential competencies for human-robot collaboration in the construction industry through a sociotechnical systems theory perspective. The construction industry grapples with significant challenges, including a shortage of skilled workers, low productivity, efficiency, and safety issues that impede its progress and growth. The integration of robots into the construction industry presents a promising solution to address these issues, thereby necessitating collaboration between humans and robots in executing construction tasks. Despite the advantages and roles played by robotic automation, there have been scarce efforts to identify essential competencies required to prepare the current and future workforce for effective collaboration with robots in construction. This study fills this gap by identifying essential competencies in the form of knowledge, skills, and abilities necessary for successful human-robot collaboration in construction. Using the sociotechnical systems theory as a framework, a qualitative literature review was conducted to establish and correlate the constructs of sociotechnical systems theory and elements of human-robot collaboration. Content analysis was employed to identify the elements of sociotechnical systems theory, human-robot collaboration, and robot task applications in construction, leading to the identification of key competencies. The study reveals a set of competencies for effective human-robot collaboration, including twenty knowledge, ten skills, and twelve abilities essential for implementing human-robot collaboration in the construction industry. These findings offer valuable insights for designing training programs and developing guidelines to facilitate successful human-robot collaboration in the construction industry. The competency model unveiled in the study could be incorporated into construction engineering and management curricula, providing a foundation for developing training initiatives targeting the current workforce and preparing the future workforce for collaborative engagements with robots in the construction industry. Recognizing the specific knowledge, skills, and abilities needed for human-robot collaboration in construction is pivotal for enhancing the efficiency and success of robotics implementation in the industry. Integrating these competencies into educational curricula and professional development programs equips the workforce to adapt to technological advancements and positions the industry for sustainable growth and improved project outcomes.
AB - This research-to-practice full paper identifies the essential competencies for human-robot collaboration in the construction industry through a sociotechnical systems theory perspective. The construction industry grapples with significant challenges, including a shortage of skilled workers, low productivity, efficiency, and safety issues that impede its progress and growth. The integration of robots into the construction industry presents a promising solution to address these issues, thereby necessitating collaboration between humans and robots in executing construction tasks. Despite the advantages and roles played by robotic automation, there have been scarce efforts to identify essential competencies required to prepare the current and future workforce for effective collaboration with robots in construction. This study fills this gap by identifying essential competencies in the form of knowledge, skills, and abilities necessary for successful human-robot collaboration in construction. Using the sociotechnical systems theory as a framework, a qualitative literature review was conducted to establish and correlate the constructs of sociotechnical systems theory and elements of human-robot collaboration. Content analysis was employed to identify the elements of sociotechnical systems theory, human-robot collaboration, and robot task applications in construction, leading to the identification of key competencies. The study reveals a set of competencies for effective human-robot collaboration, including twenty knowledge, ten skills, and twelve abilities essential for implementing human-robot collaboration in the construction industry. These findings offer valuable insights for designing training programs and developing guidelines to facilitate successful human-robot collaboration in the construction industry. The competency model unveiled in the study could be incorporated into construction engineering and management curricula, providing a foundation for developing training initiatives targeting the current workforce and preparing the future workforce for collaborative engagements with robots in the construction industry. Recognizing the specific knowledge, skills, and abilities needed for human-robot collaboration in construction is pivotal for enhancing the efficiency and success of robotics implementation in the industry. Integrating these competencies into educational curricula and professional development programs equips the workforce to adapt to technological advancements and positions the industry for sustainable growth and improved project outcomes.
KW - CEM Curriculum
KW - Competency development
KW - Construction industry
KW - Human-robot collaboration
KW - Sociotechnical systems theory (STS)
KW - Workforce development
UR - https://www.scopus.com/pages/publications/105000705305
UR - https://www.scopus.com/pages/publications/105000705305#tab=citedBy
U2 - 10.1109/FIE61694.2024.10893071
DO - 10.1109/FIE61694.2024.10893071
M3 - Conference contribution
AN - SCOPUS:105000705305
T3 - Proceedings - Frontiers in Education Conference, FIE
BT - 2024 IEEE Frontiers in Education Conference, FIE 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 October 2024 through 16 October 2024
ER -