• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.331-332
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• 2022

본 논문에서는 3축 가속도와 3축 각속도 센서로 구성된 관성 측정 장치(IMU)와 압력센서가 내장되어있는 스마트 인솔을 착용하여 얻어진 보행 데이터를 통해 보폭을 추정하는 방법을 제안한다. 먼저 압력센서를 활용하여 한 걸음 주기로 나눈 뒤 나누어진 가속도와 각속도 센서 데이터를 LSTM과 Attention 계층을 결합한 딥러닝 모델에 학습하여 보폭 추정을 시행하였다. LSTM-Attention 모델은 기존 LSTM 모델보다 약 1.14%의 성능 향상을 보였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.6
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• pp.1235-1241
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• 2012

In this paper, each driving motor for leg joints on a robot is controlled by estimating the direction of the legs measuring each joint angle and attitude angle of robot. We used quadruped working robot named TITAN-VIII in order to carry out this experimental study. 4 load cells are installed under the bottom of 4 legs to measure the pressed force on each leg while it's walking. The walking experiments of the robot were performed in 8 different conditions combined with duty factor, the length of a stride, the trajectory height of the foot and walking period of robot. The validity of attitude control for quadruped walking robot is evaluated by comparing the pressed force on a leg and the power consumption of joint driving motor. As a result, it was confirmed that the slip-condition of which the foot leave the ground late at the beginning of new period of the robot during walking process, which means the attitude control of the robot during walking process wasn't perfect only by measuring joint and attitude angle for estimating the direction of the foot.