협동로봇의 건전성 관리를 위한 머신러닝 알고리즘의 비교 분석
- Abstract
- In this paper, we propose a method for diagnosing overload and working load of collaborative robots
through performance analysis of machine learning algorithms. To this end, an experiment was conducted to
perform pick & place operation while changing the payload weight of a cooperative robot with a payload
capacity of 10 kg. In this experiment, motor torque, position, and speed data generated from the robot
controller were collected, and as a result of t-test and f-test, different characteristics were found for
each weight based on a payload of 10 kg. In addition, to predict overload and working load from the
collected data, machine learning algorithms such as Neural Network, Decision Tree, Random Forest, and
Gradient Boosting models were used for experiments. As a result of the experiment, the neural network
with more than 99.6% of explanatory power showed the best performance in prediction and classification.
The practical contribution of the proposed study is that it suggests a method to collect data required for
analysis from the robot without attaching additional sensors to the collaborative robot and the usefulness
of a machine learning algorithm for diagnosing robot overload and working load.
- Author(s)
- 장길상; 김재은; 임국화
- Issued Date
- 2021
- Type
- Article
- Keyword
- Cooperative robot; Robot data; Machine learning; Artificial neural network; Overload prediction; Work load prediction
- URI
- https://oak.ulsan.ac.kr/handle/2021.oak/9328
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_koreascholar_journals_411909&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,%ED%98%91%EB%8F%99%EB%A1%9C%EB%B4%87%EC%9D%98%20%EA%B1%B4%EC%A0%84%EC%84%B1%20%EA%B4%80%EB%A6%AC%EB%A5%BC%20%EC%9C%84%ED%95%9C%20%EB%A8%B8%EC%8B%A0%EB%9F%AC%EB%8B%9D%20%EC%95%8C%EA%B3%A0%EB%A6%AC%EC%A6%98%EC%9D%98%20%EB%B9%84%EA%B5%90%20%EB%B6%84%EC%84%9D&offset=0&pcAvailability=true
- Publisher
- 대한안전경영과학회지
- Location
- 대한민국
- Language
- 한국어
- ISSN
- 1229-6783
- Citation Volume
- 23
- Citation Number
- 4
- Citation Start Page
- 93
- Citation End Page
- 104
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