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
This study proposes a time-series learning approach for the real-time detection of inhomogeneity (clumping) in heterogeneous materials during robotic mixing using force-torque sensor data. Many existing robotic mixing systems execute predefined motion patterns that do not fully leverage dynamic changes in material states, resulting in limited responsiveness to transient mixing conditions. To better capture these dynamic variations, time-series force and torque data collected from a 6-axis sensor mounted on a robot arm are segmented into fixed-length temporal windows and used as model inputs. Ground-truth labels are established through offline analysis of synchronized external camera recordings. Three time-series classification models, namely a Bidirectional LSTM (BiLSTM), a Unidirectional LSTM (UniLSTM), and a 1D Convolutional Neural Network (1D-CNN), are trained and evaluated against a rule-based baseline. Experiments conducted across 43 mixing trials with proxy materials and evaluated using leave-one-out cross-validation show that all three learning-based models substantially outperform the rule-based approach, with BiLSTM achieving an average recall of 0.787, an F1-score of 0.704, and an AUC of 0.857. These findings indicate that leveraging temporal patterns in force- torque signals is more effective for clumping detection than approaches based solely on instantaneous signal values. Overall, this study demonstrates the potential of time-series learning to enhance mixing uniformity and support adaptive control strategies in autonomous robotic mixing systems.
본문·목차
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