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Coordination of groups of mobile autonomous agents using nearest neighbor rules
Open accessAbstract
In a recent Physical Review Letters article, Vicsek et al. propose a simple but compelling discrete-time model of n autonomous agents (i.e., points or particles) all moving in the plane with the same speed but with different headings. Each agent's heading is updated using a local rule based on the average of its own heading plus the headings of its "neighbors." In their paper, Vicsek et al. provide simulation results which demonstrate that the nearest neighbor rule they are studying can cause all agents to eventually move in the same direction despite the absence of centralized coordination and despite the fact that each agent's set of nearest neighbors change with time as the system evolves. This paper provides a theoretical explanation for this observed behavior. In addition, convergence results are derived for several other similarly inspired models. The Vicsek model proves to be a graphic example of a switched linear system which is stable, but for which there does not exist a common quadratic Lyapunov function.
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Cites
Horn, R. A., & Johnson, C. R. (1985) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810817 · Cited by 25,904
Horn, R. A., & Johnson, C. R. (1985) · Cited by 22,203
Godsil, C., & Royle, G. (2001) · Graduate Texts in Mathematics · doi:10.1007/978-1-4613-0163-9 · Cited by 8,154
Flocks, herds and schools: A distributed behavioral model
Reynolds, C. W. (1987) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/37401.37406 · Cited by 7,954
Novel Type of Phase Transition in a System of Self-Driven Particles
Vicsek, T., Czirók, A., Ben‐Jacob, E., Cohen, I., & Shochet, O. (1995) · Physical Review Letters · doi:10.1103/physrevlett.75.1226 · Cited by 7,395
Flocks, herds and schools: A distributed behavioral model
Reynolds, C. W. (1987) · ACM SIGGRAPH Computer Graphics · doi:10.1145/37402.37406 · Cited by 5,148
Non-negative Matrices and Markov Chains
Seneta, E. B. (1981) · Springer series in statistics · doi:10.1007/0-387-32792-4 · Cited by 2,858
Bollobás, B. (2001) · doi:10.1017/cbo9780511814068 · Cited by 2,639
Flocks, herds, and schools: A quantitative theory of flocking
Toner, J., & Tu, Y. (1998) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.58.4828 · Cited by 1,654
Long-Range Order in a Two-Dimensional Dynamical XY Model: How Birds Fly Together
Toner, J., & Tu, Y. (1995) · Physical Review Letters · doi:10.1103/physrevlett.75.4326 · Cited by 1,277
Research works printed but not linked: 4
- Grunbaum, 1994, Modeling social animal aggregations, 296
- Morse, Dwell-time switching, 176
- Morse, 2001, Virtual shells for avoiding collisions
- Tanner, 2002, Distributed coordination strategies for groups of mobile autonomous agents
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Yu, H., Shen, Y., & Xia, X. (2013) · Systems & Control Letters · doi:10.1016/j.sysconle.2013.06.011 · Cited by 104
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Pettersen, K. Y., Gravdahl, J. T., Nijmeijer, H., Pettersen, K. Y., Gravdahl, J. T., & Nijmeijer, H. (2006) · Springer eBooks · doi:10.1007/11505532 · Cited by 99
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Metadata
- Authors
- Jadbabaie, A., Lin, J., & Morse, A. S.
- Affiliations
- Yale University (US)
- Venue
- IEEE Transactions on Automatic Control
- Published
- 2003-06
- Volume
- 48
- Issue
- 6
- Pages
- 988-1001
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2165744313
- Cited by
- 8,339
- References
- 37
- Citations by year
- 2026: 114 · 2025: 184 · 2024: 244 · 2023: 281 · 2022: 265 · 2021: 288 · 2020: 336 · 2019: 364 · 2018: 429 · 2017: 501 · 2016: 500 · 2015: 554 · 2014: 640 · 2013: 556 · 2012: 571
- FWCI
- 209.52
- Type
- journal article
- Language
- en
- Open access
- green
- repository.upenn.edu
- Locations
- IEEE Transactions on Automatic Control (publishedVersion)
- Scholarly Commons (University of Pennsylvania) (submittedVersion)
- ScholarlyCommons (University of Pennsylvania) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Distributed Control Multi-Agent Systems
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Keywords
- Vicsek model, collective coordination, nearest neighbor rule, multi-agent systems, convergence analysis, switched linear systems, common quadratic Lyapunov function, distributed coordination, flocking
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