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Research Article

Interpreting Spatial Instructions for Effective Human-Robot Communication in Construction Environments

건설 현장의 효과적인 인간-로봇 커뮤니케이션을 위한 작업 형태 지시 해석

Chaeeun Lee1
,
Sungboo Yoon2
,
Moonseo Park3
,
Changbum Ahn4*
이 채은1
,
윤 성부2
,
박 문서3
,
안 창범4*
1M.S. Student, Department of Architecture and Architectural Engineering, Seoul National University
2Ph.D. Student, Department of Architecture and Architectural Engineering, Seoul National University
3Professor, Department of Architecture and Architectural Engineering, Seoul National University
4Corresponding Author, Professor, Department of Architecture and Architectural Engineering, Seoul National University
1서울대학교 대학원 건축학과 석사과정
2서울대학교 대학원 건축학과 박사과정
3서울대학교 건축학과 교수
4교신저자․서울대학교 건축학과 교수
*Corresponding Author
30 September 2024. pp. 6-13
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Abstract

When utilizing robots to perform tasks in construction sites, it is important to ensure efficient communication between humans and robots based on spatial instructions and the intuitive decision-making of the operator, in order to adapt flexibly to changes in the environment or alterations in plans. However, it is challenging to accurately conveying the intent of the operator to the robot in complex construction environments. This becomes particularly difficult when dealing with tasks involving objects with curvature or geometric patterns, which increase the complexity of robot trajectory generation. Therefore, this study proposes a method to estimate the robot trajectory from spatial instruction that include deviation, specifically for targets with various forms. It focuses on interpreting spatial instructions in circular, rectangular, and linear shapes commonly used in construction, and the proposed approach involves two steps: classifying the trajectory shapes from spatial instruction and fitting the shapes through regression analysis. The accuracy of this method was evaluated in a scenario where 8 participants collaborated with a robot to cut ceiling-mounted components using spatial instructions provided through a laser pointer. As a result, the proposed method achieved an average Root Mean Squared Error of 2.398mm, compared to 7.274mm for the conventional B-Spline method. These results suggest that the regression analysis-based approach to interpreting spatial instructions has the potential to improve safety and productivity in construction tasks that rely on design plans and layouts.

Keywords
Human-robot collaboration
Spatial instruction
Regression analysis
Least squared method

건설 현장에서 로봇을 활용하여 작업을 수행할 때, 환경의 변화나 계획 변경에 유연하게 대응하기 위해서는 작업자의 직관적 의사 결정과, 작업 형태 지시에 기반한 인간-로봇 간의 빠르고 정확한 소통이 중요하다. 그러나, 복잡한 건설 환경에서 작업자의 의도를 로봇에 정확하게 전달하는 것은 어려우며, 특히 곡률이나 3차원의 패턴을 가진 작업 대상은 로봇의 궤적 생성에 어려움을 더한다. 이에 본 연구는 다양한 형상의 부재에 대하여, 불안정성을 포함하는 작업 형태 지시로부터 작업자가 의도한 작업 궤적을 추론하는 방법을 제안한다. 이는 건설 작업에서 활용도가 높은 원형, 직사각형, 선분 형태의 작업 지시를 해석하는 것에 중점을 두며, 작업 형태 지시의 궤적 형상 분류와 회귀 분석을 통한 형상 적합의 두 단계로 구성된다. 해당 방법의 정확도는 8명의 참여자들이 레이저 포인터를 활용한 작업 형태 지시를 통해 로봇과 협업하여 천장에 위치한 부재를 절단하는 시나리오에서 평가하였다. 결과적으로, 제안된 방법은 평균 2.398mm의 평균 제곱근 오차로 기존 B-Spline 방법(7.274mm)보다 낮은 오차를 기록했다. 이러한 결과는 회귀 분석 기반의 작업 형태 지시 해석 방법이 도안을 바탕으로 하는 건설 작업을 효율화함으로써 작업 안전과 생산성을 향상할 수 있는 가능성을 시사한다.

키워드
인간-로봇 협업
작업 형태 지시
회귀 분석
최소제곱법
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Information
  • Publisher :Korean Society of Automation and Robotics in Construction
  • Publisher(Ko) :(사)한국건설자동화·로보틱스학회
  • Journal Title :Journal of Construction Automation and Robotics
  • Journal Title(Ko) :건설자동화·로보틱스 논문집
  • Volume : 3
  • No :3
  • Pages :6-13
  • Received Date : 2024-09-11
  • Revised Date : 2024-09-13
  • Accepted Date : 2024-09-19
  • DOI :https://doi.org/10.55785/JCAR.3.3.6
Journal Informaiton Journal of Construction Automation and Robotics Journal of Construction Automation and Robotics
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