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협동로봇의 건전성 관리를 위한 머신러닝 알고리즘의 비교 분석

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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)
장길상김재은임국화
Issued Date
2021
Type
Article
Keyword
Cooperative robotRobot dataMachine learningArtificial neural networkOverload predictionWork 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
Appears in Collections:
Business > Business Administration
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