Positioning error compensation method for industrial robots based on stacked ensemble learning Article Swipe
Qizhi Chen
,
Chengrui Zhang
,
Wei Ma
,
Chen Yang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4446368/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4446368/v1
Due to the advantages of low cost, high flexibility and large workspace, industrial robot has been considered to be the most promising plan to replace traditional CNC machine tool. However, the low absolute positioning accuracy of robot is a key factor that restricts further application in high-precision metal cutting scenarios. In order to improve the absolute positioning accuracy of robot, a positioning error compensation method based on the stacked ensemble learning is proposed. Firstly, the sources of positioning errors and compensation strategies are clarified by analyzing the kinematic model and structural composition of industrial robot. Then, based on the stacked ensemble learning algorithm, robot positioning error prediction model containing multi-layer learners is constructed. And a discrete grid optimization method is presented for model hyper-parameters optimization calculation. Next, predicted positioning errors are adopted to the realize the positioning compensation by offline compensation method. Finally, by set up a robotic milling platform based on MOTOMAN ES165D robot, a series of error compensation experiments have been implemented to verify the proposed method. After compensation, the maximum absolute position error and average position error have decreased by 83% and 89% respectively in the compensation experiments of single point. Moreover, the error compensation of the end milling experiments has also brought significant accuracy improvement, which proved the effectiveness of the proposed method in robotic machining.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-4446368/v1
- https://www.researchsquare.com/article/rs-4446368/latest.pdf
- OA Status
- gold
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399383244
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399383244Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-4446368/v1Digital Object Identifier
- Title
-
Positioning error compensation method for industrial robots based on stacked ensemble learningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-06Full publication date if available
- Authors
-
Qizhi Chen, Chengrui Zhang, Wei Ma, Chen YangList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-4446368/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-4446368/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-4446368/latest.pdfDirect OA link when available
- Concepts
-
Compensation (psychology), Ensemble learning, Robot, Computer science, Artificial intelligence, Computer vision, Machine learning, Psychology, PsychoanalysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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37Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.proved | 211 |
| abstract_inverted_index.robot, | 60, 155 |
| abstract_inverted_index.robot. | 95 |
| abstract_inverted_index.series | 157 |
| abstract_inverted_index.single | 193 |
| abstract_inverted_index.verify | 166 |
| abstract_inverted_index.MOTOMAN | 153 |
| abstract_inverted_index.adopted | 132 |
| abstract_inverted_index.average | 178 |
| abstract_inverted_index.brought | 206 |
| abstract_inverted_index.cutting | 49 |
| abstract_inverted_index.further | 44 |
| abstract_inverted_index.improve | 54 |
| abstract_inverted_index.machine | 28 |
| abstract_inverted_index.maximum | 173 |
| abstract_inverted_index.method. | 142, 169 |
| abstract_inverted_index.milling | 149, 202 |
| abstract_inverted_index.offline | 140 |
| abstract_inverted_index.realize | 135 |
| abstract_inverted_index.replace | 25 |
| abstract_inverted_index.robotic | 148, 219 |
| abstract_inverted_index.sources | 76 |
| abstract_inverted_index.stacked | 69, 100 |
| abstract_inverted_index.Finally, | 143 |
| abstract_inverted_index.Firstly, | 74 |
| abstract_inverted_index.However, | 30 |
| abstract_inverted_index.absolute | 33, 56, 174 |
| abstract_inverted_index.accuracy | 35, 58, 208 |
| abstract_inverted_index.discrete | 116 |
| abstract_inverted_index.ensemble | 70, 101 |
| abstract_inverted_index.learners | 111 |
| abstract_inverted_index.learning | 71, 102 |
| abstract_inverted_index.platform | 150 |
| abstract_inverted_index.position | 175, 179 |
| abstract_inverted_index.proposed | 168, 216 |
| abstract_inverted_index.Moreover, | 195 |
| abstract_inverted_index.analyzing | 86 |
| abstract_inverted_index.clarified | 84 |
| abstract_inverted_index.decreased | 182 |
| abstract_inverted_index.kinematic | 88 |
| abstract_inverted_index.predicted | 128 |
| abstract_inverted_index.presented | 121 |
| abstract_inverted_index.promising | 22 |
| abstract_inverted_index.proposed. | 73 |
| abstract_inverted_index.restricts | 43 |
| abstract_inverted_index.advantages | 4 |
| abstract_inverted_index.algorithm, | 103 |
| abstract_inverted_index.considered | 17 |
| abstract_inverted_index.containing | 109 |
| abstract_inverted_index.industrial | 13, 94 |
| abstract_inverted_index.machining. | 220 |
| abstract_inverted_index.prediction | 107 |
| abstract_inverted_index.scenarios. | 50 |
| abstract_inverted_index.strategies | 82 |
| abstract_inverted_index.structural | 91 |
| abstract_inverted_index.workspace, | 12 |
| abstract_inverted_index.application | 45 |
| abstract_inverted_index.composition | 92 |
| abstract_inverted_index.experiments | 161, 191, 203 |
| abstract_inverted_index.flexibility | 9 |
| abstract_inverted_index.implemented | 164 |
| abstract_inverted_index.multi-layer | 110 |
| abstract_inverted_index.positioning | 34, 57, 62, 78, 105, 129, 137 |
| abstract_inverted_index.significant | 207 |
| abstract_inverted_index.traditional | 26 |
| abstract_inverted_index.calculation. | 126 |
| abstract_inverted_index.compensation | 64, 81, 138, 141, 160, 190, 198 |
| abstract_inverted_index.constructed. | 113 |
| abstract_inverted_index.improvement, | 209 |
| abstract_inverted_index.optimization | 118, 125 |
| abstract_inverted_index.respectively | 187 |
| abstract_inverted_index.compensation, | 171 |
| abstract_inverted_index.effectiveness | 213 |
| abstract_inverted_index.high-precision | 47 |
| abstract_inverted_index.hyper-parameters | 124 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.09148014 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |