인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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.
피인용 0
초록·키워드
Manufacturing and logistics processes often involve settings in which the type, quantity, and sequence of target objects can vary, yet widely used visual servoing techniques may not accommodate such variability. To address this limitation, recent studies have explored end-to-end learning-based VLA (Vision Language Action) models; however, maintaining high success rates and motion precision under changing object placements and task sequences may require demonstration data covering diverse process conditions, thereby increasing data-collection effort. To reduce this burden, this study proposes a hybrid framework for process automation composed of two complementary modules. The interpretation of variable task instructions and object pose estimation are performed by a learning-based task and object awareness module, while motion generation, which requires precision and reliability, is handled by a model-based manipulation module. Based on the estimated pose, kinematic feasibility is evaluated in terms of inverse kinematics, joint limits, and manipulability to determine task-specific motion targets. The model-based manipulation module also provides the required workspace in confined environments through torso-inclusive motion without base movement. The proposed framework was evaluated in two process-simulating tasks, achieving part-level success rates of 94.4% for part sorting and 98.0% for part insertion. Compared with a VLA-based method on the insertion task under the same conditions, it improved part-level success from 89.0% to 98.0% and reduced the average task time from 206.6 s to 173.2 s.
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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