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Measuring Rebar Position Error and Marking Work for Automated Layout Robot Using LiDAR Sensor

마킹 로봇의 자동화를 위한 LiDAR 센서 기반 철근배근 오차 측정 및 먹매김 수행 프로세스 연구

  • Kim, Taehoon (Department of Architectural Engineering, Chosun University) ;
  • Lim, Hyunsu (Department of Architecture, Soonchunhyang University) ;
  • Cho, Kyuman (Department of Architectural Engineering, Chosun University)
  • Received : 2022.12.20
  • Accepted : 2023.01.16
  • Published : 2023.04.20

Abstract

Ensuring accuracy within tolerance is crucial for a marking robot; however, rebar displacement frequently occurs during the structural work process, necessitating corrections to layout lines or rebar locations. To guarantee precision and automation, the marking robot must be capable of measuring rebar error and determining appropriate adjustments for marking lines and rebar placement. Consequently, this study proposes a method for measuring rebar location error using a LiDAR sensor and implementing a layout assessment process based on the measurement results. The rebar recognition experiment using the LiDAR sensor yielded an average error of 5mm, demonstrating a reliable level of accuracy for wall rebars. Additionally, this research proposed a process that enables the robot to evaluate rebar and marking corrections based on the error range. The findings of this study can contribute to the automated operation of marking robots while accounting for construction errors, potentially leading to improvements in structural quality.

먹매김 로봇은 허용오차 이내의 정밀도를 확보하는 것이 매우 중요하다. 그러나 골조공사는 시공과정에서 철근배근의 변위가 빈번하게 발생하며, 해당오차는 먹선이나 철근위치의 수정을 요구한다. 먹매김 로봇은 정밀도 확보 및 자동화를 위해 철근의 오차를 측정하고 먹선과 철근의 수정을 스스로 판단할 수 있어야 한다. 이에 본 연구는 LiDAR 센서를 통한 철근배근의 오차 측정방안과 이를 바탕으로 먹매김 판단 프로세스를 제시하였다. LiDAR 센서를 활용한 철근인식 실험결과 평균적으로 5mm 내외의 오차를 발생하였으며, 이는 일반적으로 벽체에 적용되는 철근 수준에서 인식을 신뢰할만한 수준으로 나타났다. 또한 철근오차를 범위별로 판단하여 철근의 보정여부와 먹매김의 수행여부를 로봇이 스스로 판단할 수 있는 프로세스를 제시하였다. 본 연구결과는 시공오차를 고려한 먹매김로봇의 자동운영에 기여할 수 있으며 이를 통해 골조품질을 향상시킬 수 있을 것으로 기대된다.

Keywords

Acknowledgement

This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport(Grant 21CTAP-C163790-01) and by research fund from Chosun University, 2022.

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