지적과 국토정보 (Journal of Cadastre & Land InformatiX)

  • 제52권2호
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  • Pages.17-34
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  • 2022
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  • 2508-3384(pISSN)
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  • 2508-3392(eISSN)
한국국토정보공사 공간정보연구원 (LX Spatial Information Research Institute)
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로봇 친화형 건축물 인증 지표 개발 : 초점집단면접(FGI)과 분석적 계층화 과정(AHP)의 활용

Developing an Evaluation System for Certifying the Robot-Friendliness of Buildings through Focus Group Interviews and the Analytic Hierarchy Process

  • 이관용 (연세대학교 도시공학과 / 이지스자산운용) ;
  • 구한민 (연세대학교 도시공학과) ;
  • 이윤서 (연세대학교 도시공학과 / 칸서스자산운용 ) ;
  • 정민승 (연세대학교 도시공학과) ;
  • 윤동근 (연세대학교 도시공학과) ;
  • 김갑성 (연세대학교 도시공학과)
  • Lee, Kwanyong ( Department of Urban Engineering and Planning, Yonsei University / IGIS Asset Management) ;
  • Gu, Hanmin (Department of Urban Engineering and Planning, Yonsei University) ;
  • Lee, Yoonseo (Department of Urban Engineering and Planning, Yonsei University / Consus Asset Management) ;
  • Jung, Minseung (Department of Urban Engineering and Planning, Yonsei University) ;
  • Yoon, Dongkeun (Department of Urban Engineering and Planning, Yonsei University) ;
  • Kim, Kabsung (Department of Urban Engineering and Planning, Yonsei University)
  • 투고 : 2022.10.07
  • 심사 : 2022.11.22
  • 발행 : 2022.12.10
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초록

4차 산업혁명의 진전으로 로봇과 인간의 상호작용에 대한 관심이 커지고 있다. 이에 건축물의 설비와 시스템에도 로봇이 적극적으로 도입되고 있다. 저자들은 로봇 친화형 건축물 인증 지표를 개발하고자 본 연구를 수행한다. 해당 지표는 세계 최초로 개발되는 것이므로 업무용 건축물로 대상을 한정하고, 초점집단면접(FGI), 분석적 계층화 과정(AHP) 등의 방법론을 활용하여 탐색적으로 연구를 수행한다. 먼저 초점집단면접을 통하여 로봇 친화형 건축물을 개념적으로 정의하고, 건축 요건을 운영 설비 및 체계의 적절성, 건축·로봇 운영 시스템 및 네트워크의 적절성으로 분류하였다. 그다음, 분석적 계층화 과정을 통하여 전체 23개의 평가 항목에 대한 상대적 중요도를 산출하였다. 배점은 평균 4.4 그리고 최소 2.0, 최대 11.3의 범위로 계산되었다. 본 연구는 과학적 방법론을 활용하여 세계 최초의 로봇 친화형 건축물 인증 지표를 개발하는 데 필요한 기초자료를 구축하였다는 데 의의가 있다.

With rapid advancements taking place in the Fourth Industrial Revolution, human-robot interactions have been garnering increasing attention. Robots are being actively adopted in building systems and facilities. In this study, we developed robot-friendly building certification indicators. Because these indicators were being developed for the first time, we focused only on commercial buildings. We conducted exploratory research using methodologies such as focus group interviews and the analytic hierarchy process. First, the concept of the robot-friendly building was defined through focus group interviews, and the requirements were categorized by the appropriateness of operating facilities and systems and the appropriateness of architectural and robot operating systems and networks. Next, the relative importance of the evaluation items (23 items in total) was calculated using the analytic hierarchy process. Their average score of the marks was 4.4, and the minimum and maximum were 2.0 and 11.3, respectively. This study is significant because we collected the basic data necessary to develop a one-of-its-kind evaluation system for certifying the robot-friendliness of buildings using scientific methods.

키워드

과제정보

본 연구는 국토교통부의 스마트시티 혁신인재육성사업으로 지원되었음. 교신저자 김갑성은 현재 스마트도시협회에서 부여하고 있는 로봇 친화형 건축물 인증 지표 개발 프로젝트에 연구책임자로 참여한 바 있음. 여기에 함께 참여한 서울대학교 기계항공공학부 이동준 교수, 한국과학기술원(KAIST) 건설및환경공학과 김아영 교수께 깊이 감사드림. 또한 e점집단면접, 분석적 계층화 과정의 설문에 참여한 전문가 그리고 두 차례에 걸친 자문회에서 고견을 들려주신 전문가들께도 사의를 표함.

참고문헌

  1. Ministry of Science and ICT, Korea Institute of Science & Technology Evaluation and Planning. 2020. Science, ICT Policy and Technology Trends.
  2. Kim, S., Kim, J. 2017. A Study on Developing Assessment Indicators for Cyber Resilience. Journal of Digital Convergence. 15(8): 137-144. https://doi.org/10.14400/JDC.2017.15.8.137
  3. Kim, S., Choi, J. 2019. A Study on the Development of Robot Friendly Apartment Unit Plan. Proceedings of Autumn Annual Conference of Korean Housing Association (KHA). 31(2): 431-434.
  4. Park, S. 2014. An Exploratory Study on the Measuring Outcomes of Community Participatory Projects. Korea Association Of Community Welfare Studies. 49: 285-306.
  5. Ministry of Trade, Industry and Energy, Korea Evaluation Institute of Industrial Technology. 2017. Technology Roadmap of Korea.
  6. Lee, I., Jeong, H., Yeon, S. A Study on Building Object Change Detection Using Spatial Information: Building DB based on Road Name Address. Journal of Cadastre & Land InformatiX. 52(1): 105-118.
  7. Lee, I., Han, S. 2010. Exploration of the Evaluation Indicators of U-Learning Initiatives. Journal of Korean Association for Educational Information and Media. 16(2): 145-176.
  8. Lee, J., Lee, I., Kang, J. A Study on Position Matching Technique for 3D Building Model Using Existing Spatial Data. Journal of Cadastre & Land InformatiX. 51(1): 67-77. https://doi.org/10.22640/LXSIRI.2021.51.1.67
  9. Jo, H., Lee, J., Kwak, S. 2020. Calibration of Initial Position Error of 3D LiDAR for Autonomous Vehicles. Journal of Korean Institute of Intelligent Systems. 30(6): 417-423. https://doi.org/10.5391/JKIIS.2020.30.6.417
  10. Joo, E., Kim, D., Cho, B., Kim, M. 2021. Implementation of Autonomous Driving Simulation System Using 3D Spatial Data for Various Imagery Data Collection. Journal of Korean Society for Geospatial Information Science. 29(3): 3-12.
  11. Joo, E., Kim, M. Real-Time Traffic Light Information Recognition Based on Object Detection Models. Journal of Cadastre & Land InformatiX. 52(1): 81-93.
  12. Ministry of SMEs and Startups, Korea Technology and Information Promotion Agency for SMEs, NICE Information Service. 2018. 2018-2020 Technology Roadmap for SMEs.
  13. Han, S., Yoo, S. An Analysis of 3D Mesh Accuracy and Completeness of Combination of Drone and Smartphone Images for Building 3D Modeling. Journal of Cadastre & Land InformatiX. 52(1): 69-80.
  14. Hwang, J., Ahn, K., Kang, Y. 2019. Validation of Localization Method for Autonomous Vehicles Using Road Feature Map and 3D LiDAR Sensor. Journal of Institute of Control, Robotics and Systems. 5(6): 557-564. https://doi.org/10.5302/J.ICROS.2019.19.0015
  15. Alsamhi, S., Ma, O., Ansari, M. 2020. Convergence of Machine Learning and Robotics Communication in Collaborative Assembly: Mobility, Connectivity and Future Perspectives. Journal of Intelligent & Robotic Systems. 98: 541-566. https://doi.org/10.1007/s10846-019-01079-x
  16. Beer, J., Fisk, A., Rogers, W. 2014. Toward a Framework for Levels of Robot Autonomy in Human-Robot Interaction. Journal of Human-Robot Interaction. 3(2): 74-99. https://doi.org/10.5898/JHRI.3.2.Beer
  17. Buckman, A., Mayfield, M., Beck, S. 2014. What is a Smart Building? Smart and Sustainable Built Environment. 3(2): 92-109. https://doi.org/10.1108/SASBE-01-2014-0003
  18. Bodhale, D., Afzulpurkar, N., Thanh, N. 2009. Path Planning for a Mobile Robot in a Dynamic Environment. Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics. 2115-2120.
  19. Gaultney, L., Skibniewski, M., Salvendy, G. 1989. A Systematic Approach to Industrial Technology Transfer: A Conceptual Framework and a Proposed Methodology. Journal of Information Technology. 4: 7-16. https://doi.org/10.1057/jit.1989.2
  20. Ivanov, S., Webster, C. 2017. Designing Robot-Friendly Hospitality Facilities. Proceedings of the Scientific Conference "Tourism. Innovations. Strategies", Bourgas, Bulgaria. 74-81.
  21. Kitzinger, J. 1994. The Methodology of Focus Groups: The Importance of Interaction Between Research Participants. Sociology of Health & Illness. 16(1): 103-121. https://doi.org/10.1111/1467-9566.ep11347023
  22. Rozo, L., Silverio, J., Calinon, S., Caldwell, D. 2016. Learning Controllers for Reactive and Proactive Behaviors in Human-Robot Collaboration. Frontiers in Robotics and AI. 3: 30.
  23. Mohan, R., Rojas, N., Seah, S., Sosa, R. 2013. Design Principles for Robot Inclusive Spaces. Procee-dings of the 19th International Conference on Engineering Design (ICED13), Seoul, Korea. 4.
  24. Satty, T. 1977. A Scaling Method for Priorities in Hierarchical Structures. Journal of Mathematical Psychology. 15(3): 234-281. https://doi.org/10.1016/0022-2496(77)90033-5
  25. Loke, S. 2019. Robot-Friendly Cities[arXiv preprint]. arXiv:1019.10258.
  26. Tan, N., Mohan, R., Watanabe, A. 2016. Toward a Framework for Robot-Inclusive Environments. Automation in Construction. 69: 68-78. https://doi.org/10.1016/j.autcon.2016.06.001
  27. Zhang, J., Patel, V. 2006. Distributed Cognition, Representation, and Affordance. Pragmatics & Cognition. 14(2): 333-341. https://doi.org/10.1075/pc.14.2.12zha