Journal article : Review
Formal modelling for multi-robot systems under uncertainty
- Abstract:
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Purpose of Review
To effectively synthesise and analyse multi-robot behaviour, we require formal task-level models which accurately capture multi-robot execution. In this paper, we review modelling formalisms for multi-robot systems under uncertainty and discuss how they can be used for planning, reinforcement learning, model checking, and simulation.Recent Findings
Recent work has investigated models which more accurately capture multi-robot execution by considering different forms of uncertainty, such as temporal uncertainty and partial observability, and modelling the effects of robot interactions on action execution. Other strands of work have presented approaches for reducing the size of multi-robot models to admit more efficient solution methods. This can be achieved by decoupling the robots under independence assumptions or reasoning over higher-level macro actions.Summary
Existing multi-robot models demonstrate a trade-off between accurately capturing robot dependencies and uncertainty, and being small enough to tractably solve real-world problems. Therefore, future research should exploit realistic assumptions over multi-robot behaviour to develop smaller models which retain accurate representations of uncertainty and robot interactions; and exploit the structure of multi-robot problems, such as factored state spaces, to develop scalable solution methods.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 881.4KB, Terms of use)
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- Publisher copy:
- 10.1007/s43154-023-00104-0
Authors
- Publisher:
- Springer Nature
- Journal:
- Current Robotics Reports More from this journal
- Volume:
- 4
- Issue:
- 3
- Pages:
- 55-64
- Publication date:
- 2023-08-15
- Acceptance date:
- 2023-06-19
- DOI:
- EISSN:
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2662-4087
- Language:
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English
- Keywords:
- Subtype:
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Review
- Pubs id:
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1777966
- Local pid:
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pubs:1777966
- Deposit date:
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2024-05-17
Terms of use
- Copyright holder:
- Street et al
- Copyright date:
- 2023
- Rights statement:
- © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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