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RISSIn this paper, the repeated learning technique of neural network was used for gripping force control algorithm. The hybrid control system was introduced and the manipulator's finger reorganized from 2 ea to 3 ea for comfortable gripping. The data wa...
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RISS 인기검색어
반복 학습기능을 이용한 로봇 매니플레이터의 파지력제어 = The Gripping Force Control of Robot Manipulator Using the Repeated Learning Function Techniques.
한글로보기https://www.riss.kr/link?id=A3135814
- 저자
- 발행기관
- 학술지명
- 권호사항
-
발행연도
2000
-
작성언어
Korean
-
KDC
550.000
-
자료형태
학술저널
-
수록면
365-375(11쪽)
- 제공처
-
0
상세조회 -
0
다운로드
부가정보
다국어 초록 (Multilingual Abstract)
In this paper, the repeated learning technique of neural network was used for gripping force control algorithm.
The hybrid control system was introduced and the manipulator's finger reorganized from 2 ea to 3 ea for comfortable gripping.
The data was obtained using the gripping force of repeated learning techniques.
In the results of this study, the data obtained as follows;
The gripping force of egg was 0.135 [kg/cm2], the duck egg was 0.153 [kg/cm2], the quail egg was 0.045 [kg/cm2].
In the future, the adjustable gripping force will be obtained and improved the accuracy using the artificial intelligence techniques.
The hybrid control system was introduced and the manipulator's finger reorganized from 2 ea to 3 ea for comfortable gripping.
The data was obtained using the gripping force of repeated learning techniques.
In the results of this study, the data obtained as follows;
The gripping force of egg was 0.135 [kg/cm2], the duck egg was 0.153 [kg/cm2], the quail egg was 0.045 [kg/cm2].
In the future, the adjustable gripping force will be obtained and improved the accuracy using the artificial intelligence techniques.
In this paper, the repeated learning technique of neural network was used for gripping force control algorithm.
The hybrid control system was introduced and the manipulator's finger reorganized from 2 ea to 3 ea for comfortable gripping.
The data was obtained using the gripping force of repeated learning techniques.
In the results of this study, the data obtained as follows;
The gripping force of egg was 0.135 [kg/cm2], the duck egg was 0.153 [kg/cm2], the quail egg was 0.045 [kg/cm2].
In the future, the adjustable gripping force will be obtained and improved the accuracy using the artificial intelligence techniques.
목차 (Table of Contents)
- Ⅰ.서론
- Ⅱ.디관절 로봇의 하드웨어 특성
- 1.제어용 모터의 특성.
- 2.센서
- Ⅲ.힘제어 시스템의 모델링 및 구성
- Ⅰ.서론
- Ⅱ.디관절 로봇의 하드웨어 특성
- 1.제어용 모터의 특성.
- 2.센서
- Ⅲ.힘제어 시스템의 모델링 및 구성
- 1.개념
- 2.힘제어 시스템의 모델링
- 3.시스템 구성도
- Ⅳ.결과 및 고찰
- 1.실험 방법
- 2.결과 및 고찰
- Ⅴ.결론
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