Distributed Formation Control for Multi-Vehicle Systems with Splitting and Merging Capability Article Swipe
Szilard Novoth
,
Qian Zhang
,
Kang Ji
,
Dingli Yu
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/lcsys.2020.3002218
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/lcsys.2020.3002218
This letter develops a novel strategy for splitting and merging of agents travelling in formation. The method converts the formation control problem into an optimization problem, which is solved among the agents in a distributed fashion. The proposed control strategy is one type of Distributed Model Predictive Control (DMPC) which allows the system to cope with disturbances and dynamic environments. A modified Alternating Direction Method of Multipliers (ADMM) is designed to solve the trajectory optimization problem and achieve formation scaling. Furthermore, a mechanism is designed to implement path homotopy in splitting and merging of the formation, which examines the H-signature of the generated trajectories. Simulation shows that, by using the proposed method, the formation is able to automatically resize and dynamically split to better avoid obstacles, even in the case of losing communication among agents. Upon splitting the newly formed groups proceed and merge again when it becomes possible.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/lcsys.2020.3002218
- OA Status
- green
- Cited By
- 18
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3035223487
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3035223487Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/lcsys.2020.3002218Digital Object Identifier
- Title
-
Distributed Formation Control for Multi-Vehicle Systems with Splitting and Merging CapabilityWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Szilard Novoth, Qian Zhang, Kang Ji, Dingli YuList of authors in order
- Landing page
-
https://doi.org/10.1109/lcsys.2020.3002218Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://researchonline.ljmu.ac.uk/id/eprint/13243/1/Manuscript_L-CSS_v3_QZ.pdfDirect OA link when available
- Concepts
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Merge (version control), Computer science, Scaling, Path (computing), Mathematical optimization, Trajectory, Control (management), Distributed computing, Control theory (sociology), Mathematics, Artificial intelligence, Parallel computing, Programming language, Physics, Geometry, AstronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
18Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 4, 2023: 4, 2022: 2, 2021: 4Per-year citation counts (last 5 years)
- References (count)
-
22Number 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 | 60 |
| abstract_inverted_index.a | 3, 33, 81 |
| abstract_inverted_index.an | 23 |
| abstract_inverted_index.by | 107 |
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| abstract_inverted_index.to | 53, 70, 85, 116, 122 |
| abstract_inverted_index.The | 15, 36 |
| abstract_inverted_index.and | 8, 57, 76, 91, 119, 142 |
| abstract_inverted_index.for | 6 |
| abstract_inverted_index.one | 41 |
| abstract_inverted_index.the | 18, 30, 51, 72, 94, 98, 101, 109, 112, 128, 137 |
| abstract_inverted_index.This | 0 |
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| abstract_inverted_index.able | 115 |
| abstract_inverted_index.case | 129 |
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| abstract_inverted_index.novel | 4 |
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| abstract_inverted_index.solve | 71 |
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| abstract_inverted_index.that, | 106 |
| abstract_inverted_index.using | 108 |
| abstract_inverted_index.which | 26, 49, 96 |
| abstract_inverted_index.(ADMM) | 67 |
| abstract_inverted_index.(DMPC) | 48 |
| abstract_inverted_index.Method | 64 |
| abstract_inverted_index.agents | 11, 31 |
| abstract_inverted_index.allows | 50 |
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| abstract_inverted_index.groups | 140 |
| abstract_inverted_index.letter | 1 |
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| abstract_inverted_index.method | 16 |
| abstract_inverted_index.resize | 118 |
| abstract_inverted_index.solved | 28 |
| abstract_inverted_index.system | 52 |
| abstract_inverted_index.Control | 47 |
| abstract_inverted_index.achieve | 77 |
| abstract_inverted_index.agents. | 134 |
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| abstract_inverted_index.control | 20, 38 |
| abstract_inverted_index.dynamic | 58 |
| abstract_inverted_index.merging | 9, 92 |
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| abstract_inverted_index.examines | 97 |
| abstract_inverted_index.fashion. | 35 |
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| abstract_inverted_index.problem, | 25 |
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| abstract_inverted_index.strategy | 5, 39 |
| abstract_inverted_index.Direction | 63 |
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| abstract_inverted_index.mechanism | 82 |
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| abstract_inverted_index.Predictive | 46 |
| abstract_inverted_index.Simulation | 104 |
| abstract_inverted_index.formation, | 95 |
| abstract_inverted_index.formation. | 14 |
| abstract_inverted_index.obstacles, | 125 |
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| abstract_inverted_index.Alternating | 62 |
| abstract_inverted_index.Distributed | 44 |
| abstract_inverted_index.H-signature | 99 |
| abstract_inverted_index.Multipliers | 66 |
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| abstract_inverted_index.dynamically | 120 |
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| abstract_inverted_index.disturbances | 56 |
| abstract_inverted_index.optimization | 24, 74 |
| abstract_inverted_index.automatically | 117 |
| abstract_inverted_index.communication | 132 |
| abstract_inverted_index.environments. | 59 |
| abstract_inverted_index.trajectories. | 103 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.85644852 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |