Academic Journal
Multigranularity Evolutionary Method for Cooptimization of Space Robot Design and Control for Different Targets.
| Τίτλος: | Multigranularity Evolutionary Method for Cooptimization of Space Robot Design and Control for Different Targets. |
|---|---|
| Συγγραφείς: | Xie, Shucong, Zhu, Haijiang, Dong, Yunfeng |
| Πηγή: | Journal of Aerospace Engineering; May2026, Vol. 39 Issue 3, p1-13, 13p |
| Θεματικοί όροι: | Robot design & construction, Evolutionary algorithms, Subroutines (Computer programs), Multi-objective optimization, Genetic programming, Operations research, Space robotics |
| Περίληψη: | Space robots play a crucial role in on-orbit servicing tasks, including spacecraft life extension, on-orbit maintenance, and the removal of failed satellites. Their success in on-orbit capture missions depends not only on precise control strategies but also on good design schemes. Given the diverse nature of space targets, a cooptimization approach for space robot design and control tailored to different mission objectives is urgently needed. This can enhance the cost efficiency, reliability, and intelligence of space robots, ultimately strengthening their on-orbit servicing capabilities. To tackle the challenges of component-level optimization and the high computational cost associated with performance evaluation in this cooptimization process, we introduce a multigranularity evolutionary method. Specifically, we provide a general representation of space robot design and control, utilizing a genetic programming tree structure to effectively articulate component-level design schemes and control parameters. By establishing the multigranularity model, we significantly reduce the computational burden associated with performance evaluation. Additionally, we establish criteria for switching model granularity by quantifying the uncertainty within the multigranularity model, allowing for adaptive transitions during the optimization process. Numerical simulation results demonstrate that the proposed method successfully realizes the cooptimization of space robot design and control for diverse targets. Moreover, in contrast to the method of enumerating all potential morphologies and optimizing them via genetic algorithms, the proposed space robot design and control description method maintains better adjacency among similar space robots, facilitating faster convergence and yielding superior objective function values. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Aerospace Engineering is the property of American Society of Civil Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 192320891 RelevancyScore: 1061 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1060.76245117188 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Multigranularity Evolutionary Method for Cooptimization of Space Robot Design and Control for Different Targets. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xie%2C+Shucong%22">Xie, Shucong</searchLink><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Haijiang%22">Zhu, Haijiang</searchLink><br /><searchLink fieldCode="AR" term="%22Dong%2C+Yunfeng%22">Dong, Yunfeng</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Aerospace Engineering; May2026, Vol. 39 Issue 3, p1-13, 13p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Robot+design+%26+construction%22">Robot design & construction</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Subroutines+%28Computer+programs%29%22">Subroutines (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+programming%22">Genetic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Operations+research%22">Operations research</searchLink><br /><searchLink fieldCode="DE" term="%22Space+robotics%22">Space robotics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Space robots play a crucial role in on-orbit servicing tasks, including spacecraft life extension, on-orbit maintenance, and the removal of failed satellites. Their success in on-orbit capture missions depends not only on precise control strategies but also on good design schemes. Given the diverse nature of space targets, a cooptimization approach for space robot design and control tailored to different mission objectives is urgently needed. This can enhance the cost efficiency, reliability, and intelligence of space robots, ultimately strengthening their on-orbit servicing capabilities. To tackle the challenges of component-level optimization and the high computational cost associated with performance evaluation in this cooptimization process, we introduce a multigranularity evolutionary method. Specifically, we provide a general representation of space robot design and control, utilizing a genetic programming tree structure to effectively articulate component-level design schemes and control parameters. By establishing the multigranularity model, we significantly reduce the computational burden associated with performance evaluation. Additionally, we establish criteria for switching model granularity by quantifying the uncertainty within the multigranularity model, allowing for adaptive transitions during the optimization process. Numerical simulation results demonstrate that the proposed method successfully realizes the cooptimization of space robot design and control for diverse targets. Moreover, in contrast to the method of enumerating all potential morphologies and optimizing them via genetic algorithms, the proposed space robot design and control description method maintains better adjacency among similar space robots, facilitating faster convergence and yielding superior objective function values. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Aerospace Engineering is the property of American Society of Civil Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=192320891 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1061/JAEEEZ.ASENG-6635 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Robot design & construction Type: general – SubjectFull: Evolutionary algorithms Type: general – SubjectFull: Subroutines (Computer programs) Type: general – SubjectFull: Multi-objective optimization Type: general – SubjectFull: Genetic programming Type: general – SubjectFull: Operations research Type: general – SubjectFull: Space robotics Type: general Titles: – TitleFull: Multigranularity Evolutionary Method for Cooptimization of Space Robot Design and Control for Different Targets. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xie, Shucong – PersonEntity: Name: NameFull: Zhu, Haijiang – PersonEntity: Name: NameFull: Dong, Yunfeng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08931321 Numbering: – Type: volume Value: 39 – Type: issue Value: 3 Titles: – TitleFull: Journal of Aerospace Engineering Type: main |
| ResultId | 1 |