Property-Driven Design for Robot Swarms: A Design Method Based on Prescriptive Modeling and Model Checking
Abstract
In this article, we present property-driven design, a novel top-down design method for robot swarms based on prescriptive modeling and model checking. Traditionally, robot swarms have been developed using a code-and-fix approach: in a bottom-up iterative process, the developer tests and improves the individual behaviors of the robots until the desired collective behavior is obtained. The code-and-fix approach is unstructured, and the quality of the obtained swarm depends completely on the expertise and ingenuity of the developer who has little scientific or technical support in his activity. Property-driven design aims at providing such scientific and technical support, with many advantages compared to the traditional unstructured approach. Property-driven design is composed of four phases: first, the developer formally specifies the requirements of the robot swarm by stating its desired properties; second, the developer creates a prescriptive model of the swarm and uses model checking to verify that this prescriptive model satisfies the desired properties; third, using the prescriptive model as a blueprint, the developer implements a simulated version of the desired robot swarm and validates the prescriptive model developed in the previous step; fourth, the developer implements the desired robot swarm and validates the previous steps. We demonstrate property-driven design using two case studies: aggregation and foraging.
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The Swarmanoid project extended the ideas and algorithms developed in Swarm-bots to heterogeneous robot swarms composed of three types of robots-flying, climbing, and ground-based-that collaborated to carry out a search and retrieval task.
“The Swarmanoid project (2006)(2007)(2008)(2009)(2010) extended the ideas and algorithms developed in Swarm-bots to heterogeneous robot swarms composed of three types of robots-flying, climbing, and ground-based-that collaborated to carry out a search and retrieval task [28], [29].”
From Swarm Robotics: Past, Present, and Future [Point of View] (2021) · cites this paper as [29]
The Swarmanoid project demonstrated one possible direction, by studying coordinated collective behaviors in physically heterogeneous groups of robots.
“The already mentioned Swarmanoid project demonstrated one possible direction, by studying coordinated collective behaviors in physically heterogeneous groups of robots [29].”
From Swarm Robotics: Past, Present, and Future [Point of View] (2021) · cites this paper as [29]
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- Authors
- Brambilla, M., Brutschy, A., Dorigo, M., & Birattari, M.
- Affiliations
- Université Libre de Bruxelles (BE)
- Venue
- ACM Transactions on Autonomous and Adaptive Systems
- Published
- 2014-12-19
- Volume
- 9
- Issue
- 4
- Pages
- 1-28
- ISSN
- 1556-4665 (ISSN-L)
- 1556-4703
- OpenAlex
- W2054382008
- Cited by
- 66
- References
- 49
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 6 · 2022: 8 · 2021: 10 · 2020: 7 · 2019: 9 · 2018: 5 · 2017: 7 · 2016: 5 · 2015: 4
- FWCI
- 6.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Autonomous and Adaptive Systems (publishedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion)
- Funders
- Seventh Framework Programme (n.246939)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Modular Robots and Swarm Intelligence
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Formal Methods in Verification
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Keywords
- swarm robotics, prescriptive modeling, model checking, top-down design, formal specification, collective behavior, swarm aggregation, foraging behavior, multi-robot systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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