Parameters optimization of central pattern generators for hexapod robot based on multi-objective genetic algorithm Article Swipe
Binrui Wang
,
Xiaohong Cui
,
Jianbo Sun
,
Yanfeng Gao
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1177/17298814211044934
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1177/17298814211044934
In this article, a network of central pattern generators is used for the motion planning of a hexapod robot. There are many parameters in the planning network, which determine the motion performance of the hexapod robot. On the other hand, the network is a highly nonlinear coupling network, which is difficult to obtain optimal parameters by an analytical method. Optimizing these parameters to make the robot walk well is a multi-objective optimization process. There is a certain mutual exclusion relationship among the targets. To find a well-performing network as soon as possible, a multi-objective genetic algorithm is used for the process of parameter tuning. The hexapod robot simulation model is performed in Webots, and the motion performance parameters of the robot are obtained through built-in sensors and are also considered as mean values of the optimization algorithm. The optimization algorithm is written and run with MATLAB. Finally, the optimization algorithm and simulation results are proven by an experiment.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/17298814211044934
- https://journals.sagepub.com/doi/pdf/10.1177/17298814211044934
- OA Status
- gold
- Cited By
- 11
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3199466869
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3199466869Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/17298814211044934Digital Object Identifier
- Title
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Parameters optimization of central pattern generators for hexapod robot based on multi-objective genetic algorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-09-01Full publication date if available
- Authors
-
Binrui Wang, Xiaohong Cui, Jianbo Sun, Yanfeng GaoList of authors in order
- Landing page
-
https://doi.org/10.1177/17298814211044934Publisher landing page
- PDF URL
-
https://journals.sagepub.com/doi/pdf/10.1177/17298814211044934Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://journals.sagepub.com/doi/pdf/10.1177/17298814211044934Direct OA link when available
- Concepts
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Hexapod, Computer science, Genetic algorithm, Robot, Process (computing), MATLAB, Artificial neural network, Simulation, Control theory (sociology), Artificial intelligence, Machine learning, Operating system, Control (management)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 5, 2023: 4, 2022: 1Per-year citation counts (last 5 years)
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20Number 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.a | 3, 16, 43, 69, 75, 85, 92 |
| abstract_inverted_index.In | 0 |
| abstract_inverted_index.On | 36 |
| abstract_inverted_index.To | 83 |
| abstract_inverted_index.an | 56, 156 |
| abstract_inverted_index.as | 88, 90, 130 |
| abstract_inverted_index.by | 55, 155 |
| abstract_inverted_index.in | 23, 111 |
| abstract_inverted_index.is | 9, 42, 49, 68, 74, 96, 109, 140 |
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| abstract_inverted_index.and | 113, 126, 142, 150 |
| abstract_inverted_index.are | 20, 121, 127, 153 |
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| abstract_inverted_index.run | 143 |
| abstract_inverted_index.the | 12, 24, 29, 33, 37, 40, 64, 81, 99, 114, 119, 134, 147 |
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| abstract_inverted_index.make | 63 |
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| abstract_inverted_index.these | 60 |
| abstract_inverted_index.which | 27, 48 |
| abstract_inverted_index.highly | 44 |
| abstract_inverted_index.motion | 13, 30, 115 |
| abstract_inverted_index.mutual | 77 |
| abstract_inverted_index.obtain | 52 |
| abstract_inverted_index.proven | 154 |
| abstract_inverted_index.robot. | 18, 35 |
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| abstract_inverted_index.MATLAB. | 145 |
| abstract_inverted_index.Webots, | 112 |
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| abstract_inverted_index.genetic | 94 |
| abstract_inverted_index.hexapod | 17, 34, 105 |
| abstract_inverted_index.method. | 58 |
| abstract_inverted_index.network | 4, 41, 87 |
| abstract_inverted_index.optimal | 53 |
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| abstract_inverted_index.results | 152 |
| abstract_inverted_index.sensors | 125 |
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| abstract_inverted_index.written | 141 |
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| abstract_inverted_index.article, | 2 |
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| abstract_inverted_index.network, | 26, 47 |
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| abstract_inverted_index.parameter | 102 |
| abstract_inverted_index.performed | 110 |
| abstract_inverted_index.possible, | 91 |
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| abstract_inverted_index.well-performing | 86 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5102886432 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I55538621 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.7037037 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |