인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
In ship manufacturing, welding robots are used for welding automation. However, welding robots are difficult to use in curved block environments due to structural complexities. To solve this problem, accurate recognition technology of curved ship block welding environment for welding robots is required. This study proposes an objective segmentation-based geometric feature estimation framework for autonomous welding robots in curved ship block manufacturing. The geometric feature estimation of curved blocks was performed using a vision sensor, and the curvatures were estimated through the Hessian matrix, and the slopes between the parts were estimated through RANSAC (Random Simple Consensus) and SVD (Singular Value Decomposition). Finally, the gaps between the parts were estimated through segmentation and geometric feature extraction combined with DWT (discrete wavelet transform) and soft-argmax. The geometric feature estimation performance was evaluated by comparing the actual values with the estimated values through a test bed. The results of the study demonstrate that welding robots can achieve accurate and efficient welding in curved ship block. Future work will apply the proposed geometric feature estimation framework to real curved ship blocks.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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UCI(KEPA) : I410-151-26-02-094401853