Academic Journal
Hierarchical task allocation and chance-constrained trajectory optimization for heterogeneous quadrotor swarms.
| Τίτλος: | Hierarchical task allocation and chance-constrained trajectory optimization for heterogeneous quadrotor swarms. |
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| Συγγραφείς: | Huang, Yaohua, Karimi, Hamid Reza, Zhao, Xudong, Ning, Xin |
| Πηγή: | Journal of King Saud University - Computer & Information Sciences; Aug2026, Vol. 38 Issue 6, p1-22, 22p |
| Θεματικοί όροι: | Trajectory optimization, Mixed integer linear programming, Cooperation, Constraint satisfaction |
| Περίληψη: | This paper addresses the multi-task cooperative planning problem of speed-heterogeneous quadrotor UAV swarms in complex environments and proposes a hierarchical planning framework integrating task allocation and trajectory optimization. The framework aims to maximize task rewards while minimizing execution time under constraints induced by obstacles, high-risk regions, and various uncertainties. At the upper-level task allocation layer, a topology-based clustering and group-level resource matching strategy is developed. Based on task spatiotemporal constraints and heterogeneous UAV capabilities, the strategy performs macroscopic UAV-task subdomain partitioning, followed by fine-grained assignment within each local subdomain using mixed-integer linear programming (MILP). This approach achieves effective task optimization while significantly reducing computational complexity. At the lower-level trajectory generation layer, a time-optimal trajectory optimization model is formulated by considering quadrotor dynamic constraints and environmental risks. Control input uncertainties and terminal state uncertainties are modeled using chance constraints, which are transformed into deterministic constraints via an asymmetric exponential ratio approximation function. This treatment reduces conservatism while ensuring chance constraint satisfaction, thereby improving trajectory feasibility and optimality. Simulation results demonstrate that the proposed framework effectively handles cooperative task planning for heterogeneous UAV swarms, enhancing mission efficiency while ensuring safety under uncertainty. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Hierarchical task allocation and chance-constrained trajectory optimization for heterogeneous quadrotor swarms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Huang%2C+Yaohua%22">Huang, Yaohua</searchLink><br /><searchLink fieldCode="AR" term="%22Karimi%2C+Hamid+Reza%22">Karimi, Hamid Reza</searchLink><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Xudong%22">Zhao, Xudong</searchLink><br /><searchLink fieldCode="AR" term="%22Ning%2C+Xin%22">Ning, Xin</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of King Saud University - Computer & Information Sciences; Aug2026, Vol. 38 Issue 6, p1-22, 22p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Trajectory+optimization%22">Trajectory optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperation%22">Cooperation</searchLink><br /><searchLink fieldCode="DE" term="%22Constraint+satisfaction%22">Constraint satisfaction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper addresses the multi-task cooperative planning problem of speed-heterogeneous quadrotor UAV swarms in complex environments and proposes a hierarchical planning framework integrating task allocation and trajectory optimization. The framework aims to maximize task rewards while minimizing execution time under constraints induced by obstacles, high-risk regions, and various uncertainties. At the upper-level task allocation layer, a topology-based clustering and group-level resource matching strategy is developed. Based on task spatiotemporal constraints and heterogeneous UAV capabilities, the strategy performs macroscopic UAV-task subdomain partitioning, followed by fine-grained assignment within each local subdomain using mixed-integer linear programming (MILP). This approach achieves effective task optimization while significantly reducing computational complexity. At the lower-level trajectory generation layer, a time-optimal trajectory optimization model is formulated by considering quadrotor dynamic constraints and environmental risks. Control input uncertainties and terminal state uncertainties are modeled using chance constraints, which are transformed into deterministic constraints via an asymmetric exponential ratio approximation function. This treatment reduces conservatism while ensuring chance constraint satisfaction, thereby improving trajectory feasibility and optimality. Simulation results demonstrate that the proposed framework effectively handles cooperative task planning for heterogeneous UAV swarms, enhancing mission efficiency while ensuring safety under uncertainty. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of King Saud University - Computer & Information Sciences is the property of Springer Nature 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s44443-026-00797-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1 Subjects: – SubjectFull: Trajectory optimization Type: general – SubjectFull: Mixed integer linear programming Type: general – SubjectFull: Cooperation Type: general – SubjectFull: Constraint satisfaction Type: general Titles: – TitleFull: Hierarchical task allocation and chance-constrained trajectory optimization for heterogeneous quadrotor swarms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Huang, Yaohua – PersonEntity: Name: NameFull: Karimi, Hamid Reza – PersonEntity: Name: NameFull: Zhao, Xudong – PersonEntity: Name: NameFull: Ning, Xin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13191578 Numbering: – Type: volume Value: 38 – Type: issue Value: 6 Titles: – TitleFull: Journal of King Saud University - Computer & Information Sciences Type: main |
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