@article{nokey,

title = {Optimal gait design for nonlinear soft robotic crawlers},

author = {Yenan Shen and Naomi Ehrich Leonard and Bassam Bamieh and Juncal Arbelaiz},

url = {https://arxiv.org/abs/2410.17058, arXiv},

year  = {2025},

date = {2025-02-14},

urldate = {2025-02-14},

journal = {arXiv preprint arXiv:2410.17058 to appear IEEE Control Systems Letters},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Moreno-MortonEtal_2024,

title = {Fast-and-flexible decision-making with modulatory interactions},

author = {Rodrigo Moreno-Morton and Anastasia Bizyaeva and Naomi Ehrich Leonard and Alessio Franci},

url = {https://ieeexplore.ieee.org/document/10804645, IEEE Xplore

https://arxiv.org/abs/2410.00798, arXiv},

year  = {2024},

date = {2024-12-17},

urldate = {2025-02-21},

booktitle = {arXiv preprint arXiv:2410.00798},

journal = {IEEE Control Systems Letters},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Amorim2024-SpatiallyInv,

title = {Spatially-invariant opinion dynamics on the circle},

author = {Giovanna Amorim and Alessio Franci and Naomi Ehrich Leonard},

url = {https://arxiv.org/abs/2409.12420, arXiv},

year  = {2024},

date = {2024-12-11},

urldate = {2024-12-11},

booktitle = {arXiv preprint arXiv:2409.12420},

journal = {arXiv preprint arXiv:2409.12420 to appear IEEE Control Systems Letters},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cathcart2024-ENOD,

title = {Spiking Nonlinear Opinion Dynamics (S-NOD) for Agile Decision-Making},

author = {Charlotte Cathcart and Ian Xul Belaustegui and Alessio Franci and Naomi Ehrich Leonard},

url = {https://arxiv.org/abs/2409.12317, arXiv},

year  = {2024},

date = {2024-11-14},

urldate = {2024-11-14},

booktitle = {arXiv preprint arXiv:2409.12317v1},

journal = {arXiv preprint arXiv:2409.12317 to appear IEEE Control Systems Letters},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LeoBizFra_AnnualReview2024,

title = {Fast and Flexible Multiagent Decision-Making},

author = {Leonard, Naomi Ehrich and Bizyaeva, Anastasia and Franci, Alessio},

doi = {10.1146/annurev-control-090523-100059},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Annual Review of Control, Robotics, and Autonomous Systems},

volume = {7},

number = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Learning to Predict 3D Rotational Dynamics from Images of a Rigid Body with Unknown Mass Distribution },

author = {Justice J* Mason and Christine* Allen-Blanchette and Nicholas Zolman and Elizabeth Davison and Naomi Ehrich Leonard},

url = {https://www.mdpi.com/2226-4310/10/11/921},

year  = {2023},

date = {2023-10-29},

urldate = {2023-10-29},

journal = {Aerospace},

volume = {10 },

number = {921},

issue = {11},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Multi-topic belief formation through bifurcations over signed social networks},

author = {Bizyaeva, Anastasia, Alessio Franci, and Naomi Ehrich Leonard},

url = {https://arxiv.org/abs/2308.02755},

year  = {2023},

date = {2023-08-07},

urldate = {2023-08-07},

journal = {arXiv:2308.02755 [physics.soc-ph]},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Breaking indecision in multi-agent, multi-option dynamics},

author = {Alessio Franci and Martin Golubitsky and Ian Stewart and Anastasia Bizyaeva and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2023/08/FranciEtAlSIADS2023.pdf, PDF

https://arxiv.org/abs/2206.14893, arXiv},

doi = {10.1137/22M1507826},

year  = {2023},

date = {2023-08-01},

urldate = {2023-06-01},

journal = {SIAM Journal on Applied Dynamical Systems},

volume = {22},

number = {3},

pages = {1780-1817},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ArbBamLeon_LCSS2023,

title = {Structural properties of optimal risk-aware controllers for spatially invariant systems},

author = {Arbelaiz, Juncal and Bamieh, Bassam and Leonard, Naomi Ehrich},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2023/07/ArbBamLeo2023_LCSS.pdf, PDF},

doi = {10.1109/LCSYS.2023.3285873},

year  = {2023},

date = {2023-07-01},

journal = {IEEE Control Systems Letters},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ParLeo_2023,

title = {Learning with Delayed Payoffs in Population Games using Kullback-Leibler Divergence Regularization},

author = {Park, Shinkyu and Ehrich Leonard, Naomi},

url = {https://arxiv.org/abs/2306.07535, arXiv},

year  = {2023},

date = {2023-06-13},

journal = {arXiv:2306.07535 [eess.SY]},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BizFraLeoArxiv2021b,

title = {Nonlinear Opinion Dynamics with Tunable Sensitivity},

author = {Anastasia Bizyaeva and Alessio Franci and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2023/03/Nonlinear_Opinion_Dynamics_With_Tunable_Sensitivity.pdf, PDF

https://arxiv.org/abs/2009.04332, arXiv},

doi = {10.1109/TAC.2022.3159527},

year  = {2023},

date = {2023-03-01},

urldate = {2023-03-01},

journal = {IEEE Transactions on Automatic Control},

volume = {68},

number = {3},

pages = {1415-1430},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Collective intelligence as a public good},

author = {Leonard, Naomi Ehrich and Levin, Simon A},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2022/09/Leonard2022Collective-1.pdf, pdf

https://www.doi.org/10.1177/26339137221083293, doi:10.1177/26339137221083293},

year  = {2022},

date = {2022-09-13},

urldate = {2022-09-13},

journal = {Collective Intelligence},

volume = {1},

number = {1},

pages = {1--22},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9454288,

title = {Influence Spread in the Heterogeneous Multiplex Linear Threshold Model},

author = {Yaofeng Desmond Zhong and Vaibhav Srivastava and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/09/ZhoSriLeoTCNS2021.pdf, PDF

https://arxiv.org/abs/2008.04383, arXiv},

doi = {10.1109/TCNS.2021.3088782},

year  = {2022},

date = {2022-09-01},

urldate = {2021-06-14},

journal = {IEEE Transactions on Control of Network Systems},

volume = {9},

number = {3},

pages = {1080-1091},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Switching Transformations for Decentralized Control of Opinion Patterns in Signed Networks: Application to Dynamic Task Allocation},

author = {Anastasia Bizyaeva and Giovanna Amorim and María Santos and Alessio Franci and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2022/11/Switching_Transformations_for_Decentralized_Control_of_Opinion_Patterns_in_Signed_Networks_Application_to_Dynamic_Task_Allocation.pdf, PDF

https://arxiv.org/abs/2203.11703, arXiv},

doi = {10.1109/LCSYS.2022.3185981},

year  = {2022},

date = {2022-06-24},

urldate = {2022-06-24},

journal = {IEEE Control Systems Letters},

volume = {6},

note = {Presented at the 2022 Conference on Decision and Control},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Park_etalReciprocity2021,

title = {Tuning Cooperative Behavior in Games with Nonlinear Opinion Dynamics},

author = {Shinkyu Park and Anastasia Bizyaeva and Mari Kawakatsu and Alessio Franci and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2022/11/Tuning_Cooperative_Behavior_in_Games_With_Nonlinear_Opinion_Dynamics.pdf, PDF

https://arxiv.org/abs/2108.00966, arXiv},

doi = {https://doi.org/10.1109/LCSYS.2021.3138725 },

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {arXiv:2108.00966 [physics.soc-ph]},

journal = {IEEE Control Systems Letters},

volume = {6},

pages = {2030-2035},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {The nonlinear feedback dynamics of asymmetric political polarization},

author = {Naomi Ehrich Leonard and Keena Lipsitz and Anastasia Bizyaeva and Alessio Franci and Yphtach Lelkes},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/12/LeoLipBizFraLel_PNAS2021_PolarizationSI-1.pdf, pdf

https://doi.org/10.1073/pnas.2102149118

},

year  = {2021},

date = {2021-12-14},

urldate = {2021-12-14},

journal = {Proceedings of the National Academy of Sciences},

volume = {118},

number = {50},

pages = {1-9},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{FraBizParLeoArxiv2021,

title = {Analysis and control of agreement and disagreement opinion cascades},

author = {Alessio Franci and Anastasia Bizyaeva and Shinkyu Park and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/07/FraBizParLeoSwarmIntelligence2021.pdf, PDF

https://arxiv.org/abs/2103.12223, arXiv},

doi = {https://doi.org/10.1007/s11721-021-00190-w},

year  = {2021},

date = {2021-06-01},

journal = {Swarm Intelligence},

volume = {15},

number = {1},

pages = {47--82},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{MadLeoCSS2020,

title = {Heterogeneous Explore-Exploit Strategies on Multi-Star Networks},

author = {U Madhushani and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/10/MadLeoLCSS2021.pdf, PDF

https://arxiv.org/abs/2009.01339, arXiv},

doi = {10.1109/LCSYS.2020.3042459},

year  = {2021},

date = {2021-04-01},

urldate = {2021-04-01},

journal = {IEEE Control Systems Letters},

volume = {5},

number = {5},

pages = {1603-1608},

note = {Presented at the 2021 American Control Conference},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LanSriLeoAut2021,

title = {Distributed cooperative decision making in multi-agent multi-armed bandits},

author = {Peter Landgren and Vaibhav Srivastava and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/10/LanSriLeoAut2021.pdf, PDF

https://arxiv.org/abs/2003.01312, arXiv},

doi = {https://doi.org/10.1016/j.automatica.2020.109445},

issn = {0005-1098},

year  = {2021},

date = {2021-03-01},

urldate = {2021-03-01},

journal = {Automatica},

volume = {125},

pages = {109445},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PagLeoTAC2021,

title = {Adaptive Susceptibility and Heterogeneity in Contagion Models on Networks},

author = {R Pagliara and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/10/PagLeoTAC2021.pdf, PDF

https://arxiv.org/abs/1907.08829, arXiv},

doi = {10.1109/TAC.2020.2985300},

year  = {2021},

date = {2021-02-01},

urldate = {2021-02-01},

journal = {IEEE Transactions on Automatic Control},

volume = {66},

number = {2},

pages = {581-594},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{RevSriLeoCorr2021,

title = {Corrections to “Satisficing in Multiarmed Bandit Problems”},

author = {P Reverdy and V Srivastava and N E Leonard},

url = {https://ieeexplore.ieee.org/document/7795183, Original paper},

doi = {10.1109/TAC.2020.2981433},

year  = {2021},

date = {2021-01-02},

urldate = {2021-01-01},

journal = {IEEE Transactions on Automatic Control},

volume = {66},

number = {1},

pages = {476-478},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Becker_etal2021EcoMono,

title = {Ecological and behavioral mechanisms of density-dependent habitat expansion in a recovering African ungulate population},

author = {Justine A. Becker and Matthew C. Hutchinson and Arjun B. Potter and Shinkyu Park and Jennifer A. Guyton and Kyler Abernathy and Victor F. Americo and Anagledis Conceiçāo and Tyler R. Kartzinel and Luca Kuziel and Naomi E. Leonard and Eli Lorenzi and Nuno C. Martins and Johan Pansu and William L. Scott and Maria K. Stahl and Kai R. Torrens and Marc E. Stalmans and Ryan A. Long and Robert M. Pringle},

url = {https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/ecm.1476

https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/11/Becker_etal2021EcoMono.pdf, PDF},

doi = {https://doi.org/10.1002/ecm.1476},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Ecological Monographs},

pages = {e01476},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{AmiDeyDavLeoJNLS2018,

title = {Cluster Synchronization of Diffusively Coupled Nonlinear Systems: A Contraction-Based Approach},

author = {Zahra Aminzare and Biswadip Dey and Elizabeth N Davison and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/AmiDeyDavLeoJNLS2018.pdf, PDF

https://arxiv.org/abs/1707.00748, arXiv},

doi = {10.1007/s00332-018-9457-y},

year  = {2020},

date = {2020-10-01},

journal = {Journal of Nonlinear Science},

volume = {30},

pages = {2235-2257},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{FraGolBizLeoArxiv2020,

title = {A model-independent theory of consensus and dissensus decision making},

author = {Alessio Franci and Martin Golubitsky and Anastasia Bizyaeva and Naomi Ehrich Leonard},

url = {https://arxiv.org/abs/1909.05765, arXiv},

year  = {2020},

date = {2020-09-12},

journal = {arXiv:1909.05765 [math.OC]},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BizFraLeoArxiv2020,

title = {A General Model of Opinion Dynamics with Tunable Sensitivity},

author = {Anastasia Bizyaeva and Alessio Franci and Naomi Ehrich Leonard},

url = {https://arxiv.org/abs/2009.04332v2, arXiv},

year  = {2020},

date = {2020-09-09},

journal = {arXiv:2009.04332v2 [math.OC]},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PagLeoArxiv2019,

title = {Adaptive Susceptibility and Heterogeneity in Contagion Models on Networks},

author = {Renato Pagliara and Naomi E Leonard},

url = {https://arxiv.org/abs/1907.08829, arXiv},

year  = {2019},

date = {2019-09-12},

journal = {arXiv:1907.08829 [math.OC]},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{FraGolLeoArxiv2019c,

title = {The dynamics of multi-agent multi-option decision making},

author = {Alessio Franci and Martin Golubitsky and Naomi Ehrich Leonard},

url = {https://arxiv.org/abs/1909.05765, arXiv},

year  = {2019},

date = {2019-09-12},

journal = {arXiv:1909.05765 [math.OC]},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DavAmiDeyLeoChaos2019,

title = {Mixed mode oscillations and phase locking in coupled FitzHugh-Nagumo model neurons},

author = {Elizabeth N Davison and Zahra Aminzare and Biswadip Dey and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/DavAmiDeyLeoChaos2019.pdf, PDF},

doi = {10.1063/1.5050178},

year  = {2019},

date = {2019-01-01},

journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science},

volume = {29},

number = {3},

pages = {033105},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ParkJournalFieldRobotics2019,

title = {Animal-borne wireless network: Remote imaging of community ecology},

author = {Shinkyu Park and Konrad H Aschenbach and Manjur Ahmed and William L Scott and Naomi E Leonard and Kyler Abernathy and Greg Marshall and Mike Shepard and Nuno C Martins},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/Park_et_al-2019-Journal_of_Field_Robotics.pdf, PDF},

doi = {https://doi.org/10.1002/rob.21891},

year  = {2019},

date = {2019-01-01},

journal = {Journal of Field Robotics},

volume = {36},

number = {6},

pages = {1141-1165},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{TMBO_ISR2019,

title = {Social decision-making driven by artistic explore–exploit tension},

author = {Kayhan Özcimder and Biswadip Dey and Alessio Franci and Rebecca Lazier and Daniel Trueman and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/TMBOwithSIonline.pdf, PDF},

doi = {10.1080/03080188.2018.1544806},

year  = {2019},

date = {2019-01-01},

journal = {Interdisciplinary Science Reviews},

volume = {44},

number = {1},

pages = {55-81},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ScoLeoASME2018b,

title = {Minimum-Time Trajectories for Steered Agent With Constraints on Speed, Lateral Acceleration, and Turning Rate},

author = {W L Scott and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/ScoLeoASME2018.pdf, PDF},

doi = {10.1115/1.4039283},

year  = {2018},

date = {2018-07-01},

journal = {Journal of Dynamic Systems, Measurement, and Control},

volume = {140},

number = {7},

pages = {071017},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{GraFraSriLeoTCNS18,

title = {Multiagent Decision-Making Dynamics Inspired by Honeybees},

author = {R Gray and A Franci and V Srivastava and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/04/GraFraSriLeoTCNS18.pdf, PDF

https://arxiv.org/abs/1711.11578, arXiv},

doi = {10.1109/TCNS.2018.2796301},

issn = {2325-5870},

year  = {2018},

date = {2018-06-01},

journal = {IEEE Transactions on Control of Network Systems},

volume = {5},

number = {2},

pages = {793-806},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PagDeyLeoLCSS18,

title = {Bistability and Resurgent Epidemics in Reinfection Models},

author = {R Pagliara and B Dey and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/PagDeyLeoLCSS18.pdf, PDF},

doi = {10.1109/LCSYS.2018.2832063},

year  = {2018},

date = {2018-01-01},

journal = {IEEE Control Systems Letters},

volume = {2},

number = {2},

pages = {290-295},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PagGorLeoPLOS2018,

title = {Regulation of harvester ant foraging as a closed-loop excitable system},

author = {Renato Pagliara and Deborah M Gordon and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/11/PagGorLeoPLoS_CB2018.pdf, PDF},

doi = {10.1371/journal.pcbi.1006200},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {PLOS Computational Biology},

volume = {14},

number = {12},

pages = {1-25},

publisher = {Public Library of Science},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ScoLeoAutomatica2018,

title = {Optimal evasive strategies for multiple interacting agents with motion constraints},

author = {William Lewis Scott and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/ScoLeoAutomatica2018.pdf, PDF

https://doi.org/10.1016/j.automatica.2018.04.008},

issn = {0005-1098},

year  = {2018},

date = {2018-01-01},

journal = {Automatica},

volume = {94},

pages = {26 - 34},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{RevSriLeoTAC2017,

title = {Satisficing in Multi-Armed Bandit Problems},

author = {P Reverdy and V Srivastava and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/RevSriLeoTAC2017.pdf, PDF

https://arxiv.org/abs/1512.07638v2, arXiv

https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/satisficing-UCL-corrigendum_revised_submit.pdf, Paper correction

https://ieeexplore.ieee.org/document/9039571, Paper correction on IEEEXplore},

doi = {10.1109/TAC.2016.2644380},

year  = {2017},

date = {2017-01-01},

journal = {IEEE Transactions on Automatic Control},

volume = {62},

number = {8},

pages = {3788-3803},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SriFenCohLeoSheJMathPsy2017,

title = {A martingale analysis of first passage times of time-dependent Wiener diffusion models},

author = {Vaibhav Srivastava and Samuel F Feng and Jonathan D Cohen and Naomi Ehrich Leonard and Amitai Shenhav},

url = {https://arxiv.org/abs/1508.03373, arXiv},

doi = {https://doi.org/10.1016/j.jmp.2016.10.001},

issn = {0022-2496},

year  = {2017},

date = {2017-01-01},

journal = {Journal of Mathematical Psychology},

volume = {77},

pages = {94 - 110},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BruLeoLevTheorEcol2016,

title = {The content and availability of information affects the evolution of social-information gathering strategies},

author = {Eleanor Redstart Brush and Naomi Ehrich Leonard and Simon A Levin},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/BrushLeonardLevin2016.pdf, PDF

},

doi = {10.1007/s12080-016-0301-4},

year  = {2016},

date = {2016-12-01},

journal = {Theoretical Ecology},

volume = {9},

number = {4},

pages = {455-476},

abstract = {Social animals can gather information by observing the other members of their groups. Strategies for gathering this type of social information have many components. In particular, an animal can vary the number of other animals it observes. European starlings (Sturnus vulgaris) in flight pay attention to a number of neighbors that allows the flock to reach consensus quickly and robustly. The birds may do this because being in such a flock confers benefits on its members, or the birds may use the strategy that is individually beneficial without regard for the flock’s structure. To understand when individual-level optimization results in a group-level optimum, we develop a model of animals gathering social information about environmental cues, where the cue can be about either predators or resources, and we analyze two processes through which the number of neighbors changes over time. We then identify the number of neighbors the birds use when the two dynamics reach equilibrium. First, we find that the equilibrium number of neighbors is much lower when the birds are learning about the presence of resources rather than predators. Second, when the information is about the presence of predators, we find that the equilibrium number of neighbors increases as the information becomes more widespread. Third, we find that an optimization process converges on strategies that allow the flock to reach consensus when the information is about the presence of abundant resources, but not when it is about the presence of scarce resources or predators.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{FitLeoTCNS2016,

title = {Joint Centrality Distinguishes Optimal Leaders in Noisy Networks},

author = {K Fitch and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/FitLeoTCNS2016.pdf, PDF},

doi = {10.1109/TCNS.2015.2481138},

issn = {2325-5870},

year  = {2016},

date = {2016-12-01},

journal = {IEEE Transactions on Control of Network Systems},

volume = {3},

number = {4},

pages = {366-378},

abstract = {We study the performance of a network of agents tasked with tracking an external unknown signal in the presence of stochastic disturbances and under the condition that only a limited subset of agents, known as leaders, can measure the signal directly. We investigate the optimal leader selection problem for a prescribed maximum number of leaders, where the optimal leader set minimizes total system error defined as steady-state variance about the external signal. In contrast to previously established greedy algorithms for optimal leader selection, our results rely on an expression of total system error in terms of properties of the underlying network graph. We demonstrate that the performance of any given set of noise-free leaders depends on their influence as determined by a new graph measure of the centrality of a set. We define the joint centrality of a set of nodes in a network graph such that a noise-free leader set with maximal joint centrality is an optimal leader set. In the case of a single leader, we prove that the optimal leader is the node with maximal information centrality for the noise-corrupted and noise-free leader cases. In the case of multiple leaders, we show that the nodes in the optimal noise-free leader set balance high information centrality with a coverage of the graph. For special cases of graphs, we solve explicitly for optimal leader sets. Examples are used to illustrate.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{YouScaLeoTACPartI2016,

title = {A New Notion of Effective Resistance for Directed Graphs—Part I: Definition and Properties},

author = {G F Young and L Scardovi and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/YouScaLeoTACI2016.pdf, PDF},

doi = {10.1109/TAC.2015.2481978},

issn = {1558-2523},

year  = {2016},

date = {2016-07-01},

journal = {IEEE Transactions on Automatic Control},

volume = {61},

number = {7},

pages = {1727-1736},

abstract = {The graphical notion of effective resistance has found wide-ranging applications in many areas of pure mathematics, applied mathematics and control theory. By the nature of its construction, effective resistance can only be computed in undirected graphs and yet in several areas of its application, directed graphs arise as naturally (or more naturally) than undirected ones. In Part I of this work, we propose a generalization of effective resistance to directed graphs that preserves its control-theoretic properties in relation to consensus-type dynamics. We proceed to analyze the dependence of our algebraic definition on the structural properties of the graph and the relationship between our construction and a graphical distance. The results make possible the calculation of effective resistance between any two nodes in any directed graph and provide a solid foundation for the application of effective resistance to problems involving directed graphs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{YouScaLeoTACPartII2016,

title = {A New Notion of Effective Resistance for Directed Graphs—Part II: Computing Resistances},

author = {G F Young and L Scardovi and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/YouScaLeoTACII2016.pdf, PDF},

doi = {10.1109/TAC.2015.2481839},

issn = {1558-2523},

year  = {2016},

date = {2016-07-01},

journal = {IEEE Transactions on Automatic Control},

volume = {61},

number = {7},

pages = {1737-1752},

abstract = {In Part I of this work we defined a generalization of the concept of effective resistance to directed graphs, and we explored some of the properties of this new definition. Here, we use the theory developed in Part I to compute effective resistances in some prototypical directed graphs. This exploration highlights cases where our notion of effective resistance for directed graphs behaves analogously to our experience from undirected graphs, as well as cases where it behaves in unexpected ways.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PunYouMacLeoSciRep2016,

title = {Using Network Dynamical Influence to Drive Consensus},

author = {Giuliano Punzo and George F Young and Malcolm Macdonald and Naomi E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/PunYouMacLeoSciRep2016.pdf, PDF},

doi = {10.1038/srep26318},

year  = {2016},

date = {2016-05-23},

journal = {Scientific Reports},

volume = {6},

number = {1},

pages = {26318},

abstract = {Consensus and decision-making are often analysed in the context of networks, with many studies focusing attention on ranking the nodes of a network depending on their relative importance to information routing. Dynamical influence ranks the nodes with respect to their ability to influence the evolution of the associated network dynamical system. In this study it is shown that dynamical influence not only ranks the nodes, but also provides a naturally optimised distribution of effort to steer a network from one state to another. An example is provided where the “steering” refers to the physical change in velocity of self-propelled agents interacting through a network. Distinct from other works on this subject, this study looks at directed and hence more general graphs. The findings are presented with a theoretical angle, without targeting particular applications or networked systems; however, the framework and results offer parallels with biological flocks and swarms and opportunities for design of technological networks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PouYouScaLeoTAC2016,

title = {Information Centrality and Ordering of Nodes for Accuracy in Noisy Decision-Making Networks},

author = {I Poulakakis and G F Young and L Scardovi and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/PoYoScLe_TAC_16.pdf, PDF},

doi = {10.1109/TAC.2015.2454373},

issn = {1558-2523},

year  = {2016},

date = {2016-04-01},

journal = {IEEE Transactions on Automatic Control},

volume = {61},

number = {4},

pages = {1040-1045},

abstract = {This technical note considers a network of stochastic evidence accumulators, each represented by a drift-diffusion model accruing evidence towards a decision in continuous time by observing a noisy signal and by exchanging information with other units according to a fixed communication graph. These network dynamics model distributed sequential hypothesis testing as well as collective decision making. We prove the relationship between the location of each unit in the graph and its certainty as measured by the inverse of the variance of its state. Under mild connectivity assumptions, we show that only in balanced directed graphs do the node variances remain within a bounded constant from the minimum possible variance. We then prove that, for these digraphs, node ranking based on certainty is governed by information centrality, which depends on the notion of effective resistance suitably generalized to directed graphs. Our results, which describe the certainty of each unit as a function of the structural properties of the graph, can guide the selection of leaders in problems that involve the observation of noisy external signals by a cooperative multi-agent network.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{RevLeoTASE2016,

title = {Parameter Estimation in Softmax Decision-Making Models With Linear Objective Functions},

author = {P Reverdy and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/RevLeoTASE2016.pdf, PDF},

doi = {10.1109/TASE.2015.2499244},

issn = {1558-3783},

year  = {2016},

date = {2016-01-01},

journal = {IEEE Transactions on Automation Science and Engineering},

volume = {13},

number = {1},

pages = {54-67},

abstract = {We contribute to the development of a systematic means to infer features of human decision-making from behavioral data. Motivated by the common use of softmax selection in models of human decision-making, we study the maximum-likelihood (ML) parameter estimation problem for softmax decision-making models with linear objective functions. We present conditions under which the likelihood function is convex. These allow us to provide sufficient conditions for convergence of the resulting ML estimator and to construct its asymptotic distribution. In the case of models with nonlinear objective functions, we show how the estimator can be applied by linearizing about a nominal parameter value. We apply the estimator to fit the stochastic Upper Credible Limit (UCL) model of human decision-making to human subject data. The fits show statistically significant differences in behavior across related, but distinct, tasks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SwaCouLeoJNonlinearSci2015,

title = {Coordinated Speed Oscillations in Schooling Killifish Enrich Social Communication},

author = {Daniel T Swain and Iain D Couzin and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/SwaCouLeoJNLS2015.pdf, PDF},

doi = {10.1007/s00332-015-9263-8},

year  = {2015},

date = {2015-10-01},

journal = {Journal of Nonlinear Science},

volume = {25},

pages = {1077-1109},

abstract = {We examine the spatial dynamics of individuals in small schools of banded killifish (Fundulus diaphanus) that exhibit rhythmic, oscillating speed, typically with sustained, coordinated, out-of-phase speed oscillations as they move around a shallow water tank. We show that the relative motion among the fish yields a periodically time-varying network of social interactions that enriches visually driven social communication. The oscillations lead to the regular making and breaking of occlusions, which we term “switching.” We show that the rate of convergence to consensus (biologically, the capacity for individuals in groups to achieve effective coordinated motion) governed by the switching outperforms static alternatives, and performs as well as the less practical case of every fish sensing every other fish. We show further that the oscillations in speed yield oscillations in relative bearing between fish over a range that includes the angles previously predicted to be optimal for a fish to detect changes in heading and speed of its neighbors. To investigate systematically, we derive and analyze a dynamic model of interacting agents that move with oscillatory speed. We show that coordinated circular motion of the school leads to systematic cycling of spatial ordering of agents and possibilities for enriched spatial density of measurements of the external environment. Our results highlight the potential benefits of dynamic communication topologies in collective animal behavior, and suggest new, useful control laws for the distributed coordination of mobile robotic networks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Srivastava_etalArxiv2015,

title = {First passage time properties for time-varying diffusion models: A Martingale approach},

author = {Vaibhav Srivastava and Samuel F Feng and Jonathan D Cohen and Naomi Ehrich Leonard and Amitai Shenhav},

url = {https://arxiv.org/abs/1508.03373},

year  = {2015},

date = {2015-08-13},

journal = {arXiv:1508.03373 [math.PR]},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SriRevLeoArxiv2015,

title = {Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis},

author = {Naomi Ehrich Leonard Vaibhav Srivastava Paul Reverdy},

url = {https://arxiv.org/abs/1507.01160},

year  = {2015},

date = {2015-07-05},

journal = {arXiv:1507.01160 [math.OC]},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PagLeoArxiv2019,

title = {Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis},

author = {Naomi Ehrich Leonard Vaibhav Srivastava Paul Reverdy},

url = {https://arxiv.org/abs/1507.01160},

year  = {2015},

date = {2015-07-05},

journal = {arXiv:1507.01160 [math.OC]},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DavLeoOlsSchTAC2015,

title = {Nonuniform Line Coverage From Noisy Scalar Measurements},

author = {P Davison and N E Leonard and A Olshevsky and M Schwemmer},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/DavLeoOlsSchTAC2015.pdf, PDF},

doi = {10.1109/TAC.2014.2375732},

issn = {1558-2523},

year  = {2015},

date = {2015-07-01},

journal = {IEEE Transactions on Automatic Control},

volume = {60},

number = {7},

pages = {1975-1980},

abstract = {We study the problem of distributed coverage control in a network of mobile agents arranged on a line. The goal is to design distributed dynamics for the agents to achieve optimal coverage positions with respect to a scalar density field that measures the relative importance of each point on the line. Unlike previous work, which implicitly assumed the agents know this density field, we only assume that each agent can access noisy samples of the field at points close to its current location. We provide a simple randomized protocol wherein every agent samples the scalar field at three nearby points at each step and which guarantees convergence to the optimal positions. We further analyze the convergence time of this protocol and show that, under suitable assumptions, the squared distance to the optimal coverage configuration decays as O(1/t) with the number of iterations t, where the constant scales polynomially with the number of agents n. We illustrate these results with simulations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LeoOlsJConOpt2015,

title = {Cooperative Learning in Multiagent Systems from Intermittent Measurements},

author = {Naomi Ehrich Leonard and Alex Olshevsky},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/LeoOlsSICON2015.pdf, PDF},

doi = {10.1137/120891010},

year  = {2015},

date = {2015-01-01},

journal = {SIAM Journal on Control and Optimization},

volume = {53},

number = {1},

pages = {1-29},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{RevSriLeoProcIEEE2014,

title = {Modeling Human Decision Making in Generalized Gaussian Multiarmed Bandits},

author = {P B Reverdy and V Srivastava and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/RevSriLeoIEEE2014.pdf, PDF

https://arxiv.org/abs/1307.6134, arXiv version with correction},

doi = {10.1109/JPROC.2014.2307024},

issn = {1558-2256},

year  = {2014},

date = {2014-04-01},

urldate = {2014-04-01},

journal = {Proceedings of the IEEE},

volume = {102},

number = {4},

pages = {544-571},

abstract = {In this paper, we present a formal model of human decision making in explore-exploit tasks using the context of multiarmed bandit problems, where the decision maker must choose among multiple options with uncertain rewards. We address the standard multiarmed bandit problem, the multiarmed bandit problem with transition costs, and the multiarmed bandit problem on graphs. We focus on the case of Gaussian rewards in a setting where the decision maker uses Bayesian inference to estimate the reward values. We model the decision maker's prior knowledge with the Bayesian prior on the mean reward. We develop the upper-credible-limit (UCL) algorithm for the standard multiarmed bandit problem and show that this deterministic algorithm achieves logarithmic cumulative expected regret, which is optimal performance for uninformative priors. We show how good priors and good assumptions on the correlation structure among arms can greatly enhance decision-making performance, even over short time horizons. We extend to the stochastic UCL algorithm and draw several connections to human decision-making behavior. We present empirical data from human experiments and show that human performance is efficiently captured by the stochastic UCL algorithm with appropriate parameters. For the multiarmed bandit problem with transition costs and the multiarmed bandit problem on graphs, we generalize the UCL algorithm to the block UCL algorithm and the graphical block UCL algorithm, respectively. We show that these algorithms also achieve logarithmic cumulative expected regret and require a sublogarithmic expected number of transitions among arms. We further illustrate the performance of these algorithms with numerical examples.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SriLeoTCNS2014,

title = {Collective Decision-Making in Ideal Networks: The Speed-Accuracy Tradeoff},

author = {V Srivastava and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/SriLeoTCNS2014.pdf, PDF},

doi = {10.1109/TCNS.2014.2310271},

issn = {2325-5870},

year  = {2014},

date = {2014-03-01},

journal = {IEEE Transactions on Control of Network Systems},

volume = {1},

number = {1},

pages = {121-132},

abstract = {We study collective decision-making in a model of human groups, with network interactions, performing two alternative choice tasks. We focus on the speed-accuracy tradeoff, i.e., the tradeoff between a quick decision and a reliable decision, for individuals in the network. We model the evidence aggregation process across the network using a coupled drift-diffusion model (DDM) and consider the free response paradigm in which individuals take their time to make the decision. We develop a reduced DDM as a decoupled approximation to the coupled DDM and characterize its efficiency. We determine high probability bounds on the error rate and the expected decision time for the reduced DDM. We show the effect of the decision-maker's location in the network on their decision-making performance under several threshold selection criteria. Finally, we extend the coupled DDM to the coupled Ornstein-Uhlenbeck model for decision-making in two alternative choice tasks with recency effects, and to the coupled race model for decision-making in multiple alternative choice tasks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Goldbarb_etalFrontNeurosci2014,

title = {A comparative study of drift diffusion and linear ballistic accumulator models in a reward maximization perceptual choice task},

author = {Stephanie Goldfarb and Naomi E Leonard and Patrick Simen and Carlos H Caicedo-Núñez and Philip Holmes},

url = {https://www.frontiersin.org/article/10.3389/fnins.2014.00148},

doi = {10.3389/fnins.2014.00148},

issn = {1662-453X},

year  = {2014},

date = {2014-01-01},

journal = {Frontiers in Neuroscience},

volume = {8},

pages = {148},

abstract = {We present new findings that distinguish drift diffusion models (DDMs) from the linear ballistic accumulator (LBA) model as descriptions of human behavior in a two-alternative forced-choice reward maximization (Rmax) task. Previous comparisons have not considered Rmax tasks, and differences identified between the models' predictions have centered on practice effects. Unlike the parameter-free optimal performance curves of the pure DDM, the extended DDM and LBA predict families of curves depending on their additional parameters, and those of the LBA show significant differences from the DDMs, especially for poorly discriminable stimuli that incur high error rates. Moreover, fits to behavior reveal that the LBA and DDM provide different interpretations of behavior as stimulus discriminability increases. Trends for threshold setting (caution) in the DDMs are consistent between fits, while in the corresponding LBA fits, thresholds interact with distributions of starting points in a complex manner that depends upon parameter constraints. Our results suggest that reinterpretation of LBA parameters may be necessary in modeling the Rmax paradigm.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LeonardAnnualRevControl2014,

title = {Multi-agent system dynamics: Bifurcation and behavior of animal groups},

author = {Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/LeoIFACAnnualRevs14.pdf, PDF},

doi = {https://doi.org/10.1016/j.arcontrol.2014.09.002},

issn = {1367-5788},

year  = {2014},

date = {2014-01-01},

journal = {Annual Reviews in Control},

volume = {38},

number = {2},

pages = {171-183},

abstract = {Systematic design of decentralized feedback for coordinated control of multi-agent systems has much to gain from the rigorous examination of the nonlinear dynamics of collective animal behavior. Animals in groups, from bird flocks to fish schools, employ decentralized strategies and have limitations on sensing, computation, and actuation. Yet, at the level of the group, they are known to manage a variety of challenging tasks quickly, accurately, robustly and adaptively in an uncertain and changing environment. In this paper we review recent work on models and methods for studying the mechanisms of collective migration and collective decision-making in high-performing animal groups. Through bifurcation analysis we prove systematically how behavior depends on parameters that model the system and the environment. These connections lay the foundations for proving systematic control design methodologies that endow engineered multi-agent systems with the remarkable features of animal group dynamics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PaiLeoPhysicaD2014,

title = {Adaptive network dynamics and evolution of leadership in collective migration},

author = {Darren Pais and Naomi E Leonard},

url = {https://www.sciencedirect.com/science/article/pii/S0167278913001334},

doi = {https://doi.org/10.1016/j.physd.2013.04.014},

issn = {0167-2789},

year  = {2014},

date = {2014-01-01},

journal = {Physica D: Nonlinear Phenomena},

volume = {267},

pages = {81-93},

abstract = {The evolution of leadership in migratory populations depends not only on costs and benefits of leadership investments but also on the opportunities for individuals to rely on cues from others through social interactions. We derive an analytically tractable adaptive dynamic network model of collective migration with fast timescale migration dynamics and slow timescale adaptive dynamics of individual leadership investment and social interaction. For large populations, our analysis of bifurcations with respect to investment cost explains the observed hysteretic effect associated with recovery of migration in fragmented environments. Further, we show a minimum connectivity threshold above which there is evolutionary branching into leader and follower populations. For small populations, we show how the topology of the underlying social interaction network influences the emergence and location of leaders in the adaptive system. Our model and analysis can be extended to study the dynamics of collective tracking or collective learning more generally. Thus, this work may inform the design of robotic networks where agents use decentralized strategies that balance direct environmental measurements with agent interactions.},

note = {Evolving Dynamical Networks},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LeoOlsTAC2013,

title = {Nonuniform coverage control on the line},

author = {N E Leonard and A Olshevsky},

doi = {10.1109/TAC.2013.2266991},

issn = {1558-2523},

year  = {2013},

date = {2013-11-01},

journal = {IEEE Transactions on Automatic Control},

volume = {58},

number = {11},

pages = {2743-2755},

abstract = {This paper investigates control laws allowing mobile, autonomous agents to optimally position themselves on the line for distributed sensing in a nonuniform field. We show that a simple static control law, based only on local measurements of the field by each agent, drives the agents close to the optimal positions after the agents execute in parallel a number of sensing/movement/computation rounds that is essentially quadratic in the number of agents. Further, we exhibit a dynamic control law which, under slightly stronger assumptions on the capabilities and knowledge of each agent, drives the agents close to the optimal positions after the agents execute in parallel a number of sensing/communication/computation/movement rounds that is essentially linear in the number of agents. Crucially, both algorithms are fully distributed and robust to unpredictable loss and addition of agents.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pais_etalPLOSOne2013,

title = {A Mechanism for Value-Sensitive Decision-Making},

author = {Darren Pais and Patrick M Hogan and Thomas Schlegel and Nigel R Franks and Naomi E Leonard and James A R Marshall},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/11/PaisPLoS2013SI.pdf, PDF},

doi = {10.1371/journal.pone.0073216},

year  = {2013},

date = {2013-01-01},

urldate = {2013-01-01},

journal = {PLOS ONE},

volume = {8},

number = {9},

pages = {1-9},

publisher = {Public Library of Science},

abstract = {We present a dynamical systems analysis of a decision-making mechanism inspired by collective choice in house-hunting honeybee swarms, revealing the crucial role of cross-inhibitory ‘stop-signalling’ in improving the decision-making capabilities. We show that strength of cross-inhibition is a decision-parameter influencing how decisions depend both on the difference in value and on the mean value of the alternatives; this is in contrast to many previous mechanistic models of decision-making, which are typically sensitive to decision accuracy rather than the value of the option chosen. The strength of cross-inhibition determines when deadlock over similarly valued alternatives is maintained or broken, as a function of the mean value; thus, changes in cross-inhibition strength allow adaptive time-dependent decision-making strategies. Cross-inhibition also tunes the minimum difference between alternatives required for reliable discrimination, in a manner similar to Weber's law of just-noticeable difference. Finally, cross-inhibition tunes the speed-accuracy trade-off realised when differences in the values of the alternatives are sufficiently large to matter. We propose that the model, and the significant role of the values of the alternatives, may describe other decision-making systems, including intracellular regulatory circuits, and simple neural circuits, and may provide guidance in the design of decision-making algorithms for artificial systems, particularly those functioning without centralised control.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Young_etalPLOS2013,

title = {Starling Flock Networks Manage Uncertainty in Consensus at Low Cost},

author = {George F Young and Luca Scardovi and Andrea Cavagna and Irene Giardina and Naomi E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/YouStarlingsPLoS13.pdf, PDF},

doi = {10.1371/journal.pcbi.1002894},

year  = {2013},

date = {2013-01-01},

journal = {PLOS Computational Biology},

volume = {9},

number = {1},

pages = {1-7},

publisher = {Public Library of Science},

abstract = {Author Summary Starling flocks move in beautiful ways that both captivate and intrigue the observer. Previous work has shown that starlings pay attention to their seven closest neighbors, but until now it was not understood why this number is seven. Our paper explains the mystery: when uncertainty in sensing is present, interacting with six or seven neighbors optimizes the balance between group cohesiveness and individual effort. To prove this result we develop a new systems-theoretic approach for understanding noisy consensus dynamics. The approach allows the evaluation of robustness over a family of hypothesized sensing strategies using observations of the spatial positions of birds within the flock. We apply this approach to experimental data from wild starling flocks, and find that six or seven neighbors yield maximal robustness. The implication that robustness of cohesion may have been a factor in the evolution of flocking has significant consequences for evolutionary biology. In addition, the results and the versatility of the approach have implications for uncertainty management in social and technological networks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PouScaLeoArxiv2012,

title = {Node Classification in Networks of Stochastic Evidence Accumulators},

author = {Ioannis Poulakakis and Luca Scardovi and Naomi Ehrich Leonard},

url = {https://arxiv.org/abs/1210.4235},

year  = {2012},

date = {2012-10-16},

journal = {arXiv:1210.4235 [cs.SY]},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Goldfarb_etalFrontPsychol2012,

title = {Can post-error dynamics explain sequential reaction time patterns?},

author = {Stephanie Goldfarb and Kongfatt Wong-Lin and Michael Schwemmer and Naomi Ehrich Leonard and Philip Holmes},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/fpsyg-03-00213.pdf, PDF},

doi = {10.3389/fpsyg.2012.00213},

year  = {2012},

date = {2012-07-01},

journal = {Frontiers in Psychology},

volume = {3},

pages = {213},

publisher = {Frontiers Research Foundation},

abstract = {We investigate human error dynamics in sequential two-alternative choice tasks. When subjects repeatedly discriminate between two stimuli, their error rates and reaction times (RTs) systematically depend on prior sequences of stimuli. We analyze these sequential effects on RTs, separating error and correct responses, and identify a sequential RT tradeoff: a sequence of stimuli which yields a relatively fast RT on error trials will produce a relatively slow RT on correct trials and vice versa. We reanalyze previous data and acquire and analyze new data in a choice task with stimulus sequences generated by a first-order Markov process having unequal probabilities of repetitions and alternations. We then show that relationships among these stimulus sequences and the corresponding RTs for correct trials, error trials, and averaged over all trials are significantly influenced by the probability of alternations; these relationships have not been captured by previous models. Finally, we show that simple, sequential updates to the initial condition and thresholds of a pure drift diffusion model can account for the trends in RT for correct and error trials. Our results suggest that error-based parameter adjustments are critical to modeling sequential effects.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Stewart_etalProcIEEE2012,

title = {Towards Human–Robot Teams: Model-Based Analysis of Human Decision Making in Two-Alternative Choice Tasks With Social Feedback},

author = {A Stewart and M Cao and A Nedic and D Tomlin and N Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/StewartEtAl2012.pdf, PDF},

doi = {10.1109/JPROC.2011.2173815},

issn = {1558-2256},

year  = {2012},

date = {2012-03-01},

journal = {Proceedings of the IEEE},

volume = {100},

number = {3},

pages = {751-775},

abstract = {With a principled methodology for systematic design of human-robot decision-making teams as a motivating goal, we seek an analytic, model-based description of the influence of team and network design parameters on decision-making performance. Given that there are few reliably predictive models of human decision making, we consider the relatively well-understood two-alternative choice tasks from cognitive psychology, where individuals make sequential decisions with limited information, and we study a stochastic decision-making model, which has been successfully fitted to human behavioral and neural data for a range of such tasks. We use an extension of the model, fitted to experimental data from groups of humans performing the same task simultaneously and receiving feedback on the choices of others in the group. First, we show how the task and model can be regarded as a Markov process. Then, we derive analytically the steady-state probability distributions for decisions and performance as a function of model and design parameters such as the strength and path of the social feedback. Finally, we discuss application to human-robot team and network design and next steps with a multirobot testbed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Leonard_etalPNAS2012,

title = {Decision versus compromise for animal groups in motion},

author = {Naomi E Leonard and Tian Shen and Benjamin Nabet and Luca Scardovi and Iain D Couzin and Simon A Levin},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/PNAS-2012-Leonard-227-32SI.pdf, PDF},

doi = {10.1073/pnas.1118318108},

issn = {0027-8424},

year  = {2012},

date = {2012-01-01},

journal = {Proceedings of the National Academy of Sciences},

volume = {109},

number = {1},

pages = {227--232},

publisher = {National Academy of Sciences},

abstract = {Previously, we showed using a computational agent-based model that a group of animals moving together can make a collective decision on direction of motion, even if there is a conflict between the directional preferences of two small subgroups of textquotedblleftinformedtextquotedblright individuals and the remaining textquotedblleftuninformedtextquotedblright individuals have no directional preference. The model requires no explicit signaling or identification of informed individuals; individuals merely adjust their steering in response to socially acquired information on relative motion of neighbors. In this paper, we show how the dynamics of this system can be modeled analytically, and we derive a testable result that adding uninformed individuals improves stability of collective decision making. We first present a continuous-time dynamic model and prove a necessary and sufficient condition for stable convergence to a collective decision in this model. The stability of the decision, which corresponds to most of the group moving in one of two alternative preferred directions, depends explicitly on the magnitude of the difference in preferred directions; for a difference above a threshold the decision is stable and below that same threshold the decision is unstable. Given qualitative agreement with the results of the previous simulation study, we proceed to explore analytically the subtle but important role of the uninformed individuals in the continuous-time model. Significantly, we show that the likelihood of a collective decision increases with increasing numbers of uninformed individuals.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PaiCaiLeoJADS2012b,

title = {Hopf Bifurcations and Limit Cycles in Evolutionary Network Dynamics},

author = {Darren Pais and Carlos H Caicedo-Núñez and Naomi E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/PaisSIADS12.pdf, PDF},

doi = {10.1137/120878537},

year  = {2012},

date = {2012-01-01},

journal = {SIAM Journal on Applied Dynamical Systems},

volume = {11},

number = {4},

pages = {1754-1784},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SwaCouLeoProcIEEE2012,

title = {Real-Time Feedback-Controlled Robotic Fish for Behavioral Experiments With Fish Schools},

author = {D T Swain and I D Couzin and N Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/SwaCouLeo12.pdf, IEEE},

doi = {10.1109/JPROC.2011.2165449},

issn = {1558-2256},

year  = {2012},

date = {2012-01-01},

journal = {Proceedings of the IEEE},

volume = {100},

number = {1},

pages = {150-163},

abstract = {Integrating robotic agents into animal groups creates significant opportunities for advancing experimental investigations of collective animal behavior. In the case of fish schooling, new insights into processes such as collective decision making and leadership have been made in recent experiments in which live fish were interacting with robotic fish driven along preplanned paths. We introduce a new cyber-physical implementation that enables robotic fish to use real-time feedback to control their motion in response to live fish and other environmental features. Each robotic fish is magnetically connected to, and thus moved by, a wheeled robot underneath the tank. Real-time image processing of a video stream from an overhead camera provides measurements of both the robotic fish and the live fish moving together in the tank. Feedback responses computed from these measurements are communicated to the robotic fish using Bluetooth. We show results of demonstrations and discuss possibilities that our implementation affords for new kinds of behavioral experiments with fish schools.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Couzin_etalScience2011,

title = {Uninformed Individuals Promote Democratic Consensus in Animal Groups},

author = {Iain D Couzin and Christos C Ioannou and Güven Demirel and Thilo Gross and Colin J Torney and Andrew Hartnett and Larissa Conradt and Simon A Levin and Naomi E Leonard},

url = {https://science.sciencemag.org/content/334/6062/1578},

doi = {10.1126/science.1210280},

issn = {0036-8075},

year  = {2011},

date = {2011-01-01},

journal = {Science},

volume = {334},

number = {6062},

pages = {1578--1580},

publisher = {American Association for the Advancement of Science},

abstract = {Conflicting interests among group members are common when making collective decisions, yet failure to achieve consensus can be costly. Under these circumstances individuals may be susceptible to manipulation by a strongly opinionated, or extremist, minority. It has previously been argued, for humans and animals, that social groups containing individuals who are uninformed, or exhibit weak preferences, are particularly vulnerable to such manipulative agents. Here, we use theory and experiment to demonstrate that, for a wide range of conditions, a strongly opinionated minority can dictate group choice, but the presence of uninformed individuals spontaneously inhibits this process, returning control to the numerical majority. Our results emphasize the role of uninformed individuals in achieving democratic consensus amid internal group conflict and informational constraints.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ZhaLeoTAC2010,

title = {Cooperative Filters and Control for Cooperative Exploration},

author = {F Zhang and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/ZhaLeoTAC10.pdf, PDF},

doi = {10.1109/TAC.2009.2039240},

issn = {1558-2523},

year  = {2010},

date = {2010-03-01},

journal = {IEEE Transactions on Automatic Control},

volume = {55},

number = {3},

pages = {650-663},

abstract = {Autonomous mobile sensor networks are employed to measure large-scale environmental fields. Yet an optimal strategy for mission design addressing both the cooperative motion control and the cooperative sensing is still an open problem. We develop strategies for multiple sensor platforms to explore a noisy scalar field in the plane. Our method consists of three parts. First, we design provably convergent cooperative Kalman filters that apply to general cooperative exploration missions. Second, we present a novel method to determine the shape of the platform formation to minimize error in the estimates and design a cooperative formation control law to asymptotically achieve the optimal formation shape. Third, we use the cooperative filter estimates in a provably convergent motion control law that drives the center of the platform formation to move along level curves of the field. This control law can be replaced by control laws enabling other cooperative exploration motion, such as gradient climbing, without changing the cooperative filters and the cooperative formation control laws. Performance is demonstrated on simulated underwater platforms in simulated ocean fields.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{CaoSteLeoSCL2010,

title = {Convergence in human decision-making dynamics},

author = {Ming Cao and Andrew Stewart and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/CaoSteLeoSCL10.pdf, PDF},

doi = {https://doi.org/10.1016/j.sysconle.2009.12.002},

issn = {0167-6911},

year  = {2010},

date = {2010-01-01},

journal = {Systems & Control Letters},

volume = {59},

number = {2},

pages = {87-97},

abstract = {A class of binary decision-making tasks called the two-alternative forced-choice task has been used extensively in psychology and behavioral economics experiments to investigate human decision making. The human subject makes a choice between two options at regular time intervals and receives a reward after each choice; for a variety of reward structures, these experiments show convergence of the aggregate behavior to rewards that are often suboptimal. In this paper we present two models of human decision making: one is the Win-Stay, Lose-Switch (WSLS) model and the other is a deterministic limit of the popular Drift Diffusion (DD) model. With these models we prove the convergence of human behavior to the observed aggregate decision making for reward structures with matching points. The analysis is motivated by human-in-the-loop systems, where humans are often required to make repeated choices among finite alternatives in response to evolving system performance measures. We discuss application of the convergence result to the design of human-in-the-loop systems using a map from the human subject to a human supervisor.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Leo_etalJFieldRob2010,

title = {Coordinated control of an underwater glider fleet in an adaptive ocean sampling field experiment in Monterey Bay},

author = {Naomi E Leonard and Derek A Paley and Russ E Davis and David M Fratantoni and Francois Lekien and Fumin Zhang},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/LeoPalDavJFR10.pdf, PDF},

doi = {https://doi.org/10.1002/rob.20366},

year  = {2010},

date = {2010-01-01},

journal = {Journal of Field Robotics},

volume = {27},

number = {6},

pages = {718-740},

abstract = {Abstract A full-scale adaptive ocean sampling network was deployed throughout the month-long 2006 Adaptive Sampling and Prediction (ASAP) field experiment in Monterey Bay, California. One of the central goals of the field experiment was to test and demonstrate newly developed techniques for coordinated motion control of autonomous vehicles carrying environmental sensors to efficiently sample the ocean. We describe the field results for the heterogeneous fleet of autonomous underwater gliders that collected data continuously throughout the month-long experiment. Six of these gliders were coordinated autonomously for 24 days straight using feedback laws that scale with the number of vehicles. These feedback laws were systematically computed using recently developed methodology to produce desired collective motion patterns, tuned to the spatial and temporal scales in the sampled fields for the purpose of reducing statistical uncertainty in field estimates. The implementation was designed to allow for adaptation of coordinated sampling patterns using human-in-the-loop decision making, guided by optimization and prediction tools. The results demonstrate an innovative tool for ocean sampling and provide a proof of concept for an important field robotics endeavor that integrates coordinated motion control with adaptive sampling. © 2010 Wiley Periodicals, Inc.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{NabLeoArxiv2009,

title = {Tensegrity Models and Shape Control of Vehicle Formations},

author = {Benjamin Nabet and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/tenseg_ACC09_Mar22.pdf, PDF

https://arxiv.org/abs/0902.3710, arXiv},

year  = {2009},

date = {2009-02-23},

journal = {arXiv:0902.3710 [math.OC]},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DavLeoFraDeepSea2009,

title = {Routing strategies for underwater gliders},

author = {Russ E Davis and Naomi E Leonard and David M Fratantoni},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/DavisDSR09.pdf, PDF},

doi = {https://doi.org/10.1016/j.dsr2.2008.08.005},

issn = {0967-0645},

year  = {2009},

date = {2009-01-01},

journal = {Deep Sea Research Part II: Topical Studies in Oceanography},

volume = {56},

number = {3},

pages = {173-187},

abstract = {Gliders are autonomous underwater vehicles that achieve long operating range by moving at speeds comparable to those of, or slower than, typical ocean currents. This paper addresses routing gliders to rapidly reach a specified waypoint or to maximize the ability to map a measured field, both in the presence of significant currents. For rapid transit in a frozen velocity field, direct minimization of travel time provides a trajectory “ray” equation. A simpler routing algorithm that requires less information is also discussed. Two approaches are developed to maximize the mapping ability, as measured by objective mapping error, of arrays of vehicles. In order to produce data sets that are readily interpretable, both approaches focus sampling near predetermined “ideal tracks” by measuring mapping skill only on those tracks, which are laid out with overall mapping skill in mind. One approach directly selects each vehicle's headings to maximize instantaneous mapping skill integrated over the entire array. Because mapping skill decreases when measurements are clustered, this method automatically coordinates glider arrays to maintain spacing. A simpler method that relies on manual control for array coordination employs a first-order control loop to balance staying close to the ideal track and maintaining vehicle speed to maximize mapping skill. While the various techniques discussed help in dealing with the slow speed of gliders, nothing can keep performance from being degraded when current speeds are comparable to vehicle speed. This suggests that glider utility could be greatly enhanced by the ability to operate high speeds for short periods when currents are strong.},

note = {AOSN II: The Science and Technology of an Autonomous Ocean Sampling Network},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LekLeoJCO2009,

title = {Nonuniform Coverage and Cartograms},

author = {Francois Lekien and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/LekienLeonard09.pdf, PDF},

doi = {10.1137/070681120},

year  = {2009},

date = {2009-01-01},

journal = {SIAM Journal on Control and Optimization},

volume = {48},

number = {1},

pages = {351-372},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{NabLeoCouLevJNonlinear2009,

title = {Dynamics of Decision Making in Animal Group Motion},

author = {Benjamin Nabet and Naomi E Leonard and Iain Couzin & Simon D A Levin},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/NabLeoCouLev09.pdf, PDF},

doi = {10.1007/s00332-008-9038-6},

year  = {2009},

date = {2009-01-01},

journal = {Journal of Nonlinear Science},

volume = {19},

number = {4},

pages = {399-435},

abstract = {We analyze a continuous-time model of a multi-agent system motivated by simulation studies on dynamics of decision making in animal groups in motion. Each individual moves at constant speed in the plane and adjusts its heading in response to relative headings of others in the population. The population includes two subgroups that are “informed” such that individuals in each subgroup have a preferred direction of motion. The model exhibits fast and slow time scales allowing for a reduction in the dimension of the problem. The stable solutions for the reduced model correspond to compromise by individuals with conflicting preferences. We study the global phase space for the proposed reduced model by computing equilibria and exploring stability and bifurcations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ramp_etalDeepSea2009,

title = {Preparing to predict: The Second Autonomous Ocean Sampling Network (AOSN-II) experiment in the Monterey Bay},

author = {S R Ramp and R E Davis and N E Leonard and I Shulman and Y Chao and A R Robinson and J Marsden and P F J Lermusiaux and D M Fratantoni and J D Paduan and F P Chavez and F L Bahr and S Liang and W Leslie and Z Li},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/RampDSR09.pdf, PDF},

doi = {https://doi.org/10.1016/j.dsr2.2008.08.013},

issn = {0967-0645},

year  = {2009},

date = {2009-01-01},

journal = {Deep Sea Research Part II: Topical Studies in Oceanography},

volume = {56},

number = {3},

pages = {68-86},

abstract = {The Autonomous Ocean Sampling Network Phase Two (AOSN-II) experiment was conducted in and offshore from the Monterey Bay on the central California coast during July 23–September 6, 2003. The objective of the experiment was to learn how to apply new tools, technologies, and analysis techniques to adaptively sample the coastal ocean in a manner demonstrably superior to traditional methodologies, and to use the information gathered to improve predictive skill for quantities of interest to end-users. The scientific goal was to study the upwelling/relaxation cycle near an open coastal bay in an eastern boundary current region, particularly as it developed and spread from a coastal headland. The suite of observational tools used included a low-flying aircraft, a fleet of underwater gliders, including several under adaptive autonomous control, and propeller-driven AUVs in addition to moorings, ships, and other more traditional hardware. The data were delivered in real time and assimilated into the Harvard Ocean Prediction System (HOPS), the Navy Coastal Ocean Model (NCOM), and the Jet Propulsion Laboratory implementation of the Regional Ocean Modeling System (JPL/ROMS). Two upwelling events and one relaxation event were sampled during the experiment. The upwelling in both cases began when a pool of cold water less than 13°C appeared near Cape Año Nuevo and subsequently spread offshore and southward across the bay as the equatorward wind stress continued. The primary difference between the events was that the first event spread offshore and southward, while the second event spread only southward and not offshore. The difference is attributed to the position and strength of meanders and eddies of the California Current System offshore, which blocked or steered the cold upwelled water. The space and time scales of the mesoscale variability were much shorter than have been previously observed in deep-water eddies offshore. Additional process studies are needed to elucidate the dynamics of the flow.},

note = {AOSN II: The Science and Technology of an Autonomous Ocean Sampling Network},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SarSepLeoAutomatica2009,

title = {Autonomous rigid body attitude synchronization},

author = {Alain Sarlette and Rodolphe Sepulchre and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/Sarlette09.pdf, PDF},

doi = {https://doi.org/10.1016/j.automatica.2008.09.020},

issn = {0005-1098},

year  = {2009},

date = {2009-01-01},

journal = {Automatica},

volume = {45},

number = {2},

pages = {572-577},

abstract = {Control laws to synchronize attitudes in a swarm of fully actuated rigid bodies, in the absence of a common reference attitude or hierarchy in the swarm, are proposed in [Smith, T. R., Hanssmann, H., & Leonard, N.E. (2001). Orientation control of multiple underwater vehicles with symmetry-breaking potentials. In Proc. 40th IEEE conf. decision and control (pp. 4598–4603); Nair, S., Leonard, N. E. (2007). Stable synchronization of rigid body networks. Networks and Heterogeneous Media, 2(4), 595–624]. The present paper studies two separate extensions with the same energy shaping approach: (i) locally synchronizing the rigid bodies’ attitudes, but without restricting their final motion and (ii) relaxing the communication topology from undirected, fixed and connected to directed, varying and uniformly connected. The specific strategies that must be developed for these extensions illustrate the limitations of attitude control with reduced information.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PalZhaLeoTCST2008,

title = {Cooperative Control for Ocean Sampling: The Glider Coordinated Control System},

author = {D A Paley and F Zhang and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/PalZhaLeoTCST08.pdf, PDF},

doi = {10.1109/TCST.2007.912238},

issn = {1558-0865},

year  = {2008},

date = {2008-07-01},

journal = {IEEE Transactions on Control Systems Technology},

volume = {16},

number = {4},

pages = {735-744},

abstract = {The glider coordinated control system (GCCS) uses a detailed glider model for prediction and a simple particle model for planning to steer a fleet of underwater gliders to a set of coordinated trajectories. The GCCS also serves as a simulation testbed for the design and evaluation of multivehicle control laws. In this brief, we describe the GCCS and present experimental results for a virtual deployment in Monterey Bay, CA and a real deployment in Buzzards Bay, MA.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SepPalLeoTAC2008,

title = {Stabilization of Planar Collective Motion With Limited Communication},

author = {R Sepulchre and D A Paley and N E Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/seppalleo08.pdf, PDF},

doi = {10.1109/TAC.2008.919857},

issn = {1558-2523},

year  = {2008},

date = {2008-04-01},

journal = {IEEE Transactions on Automatic Control},

volume = {53},

number = {3},

pages = {706-719},

abstract = {This paper proposes a design methodology to stabilize relative equilibria in a model of identical, steered particles moving in the plane at unit speed. Relative equilibria either correspond to parallel motion of all particles with fixed relative spacing or to circular motion of all particles around the same circle. Particles exchange relative information according to a communication graph that can be undirected or directed and time-invariant or time-varying. The emphasis of this paper is to show how previous results assuming all-to-all communication can be extended to a general communication framework.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BhaLeoAutomatica2008,

title = {Nonlinear gliding stability and control for vehicles with hydrodynamic forcing},

author = {Pradeep Bhatta and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/BhattaLeonardFinalVersion.pdf, PDF},

doi = {https://doi.org/10.1016/j.automatica.2007.10.006},

issn = {0005-1098},

year  = {2008},

date = {2008-01-01},

journal = {Automatica},

volume = {44},

number = {5},

pages = {1240-1250},

abstract = {This paper presents Lyapunov functions for proving the stability of steady gliding motions for vehicles with hydrodynamic or aerodynamic forces and moments. Because of lifting forces and moments, system energy cannot be used as a Lyapunov function candidate. A Lyapunov function is constructed using a conservation law discovered by Lanchester in his classical work on phugoid-mode dynamics of an airplane. The phugoid-mode dynamics, which are cast here as Hamiltonian dynamics, correspond to the slow dynamics in a multi-time-scale model of a hydro/aerodynamically-forced vehicle in the longitudinal plane. Singular perturbation theory is used in the proof of stability of gliding motions. As an intermediate step, the simplifying assumptions of Lanchester are made rigorous. It is further shown how to design stabilizing control laws for gliding motions using the derived function as a control Lyapunov function and how to compute the corresponding regions of attraction.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{NaiLeoJCO2008,

title = {Stable Synchronization of Mechanical System Networks},

author = {Sujit Nair and Naomi Ehrich Leonard},

url = {https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/SJC000661.pdf, PDF},

doi = {10.1137/050646639},

year  = {2008},

date = {2008-01-01},

journal = {SIAM Journal on Control and Optimization},

volume = {47},

number = {2},

pages = {661-683},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PalLeoSepSCL2008,

title = {Stabilization of symmetric formations to motion around convex loops},

author = {Derek A Paley and Naomi Ehrich Leonard and Rodolphe Sepulchre},

url = {https://www.sciencedirect.com/science/article/pii/S0167691107001168},

doi = {https://doi.org/10.1016/j.sysconle.2007.08.005},

issn = {0167-6911},

year  = {2008},

date = {2008-01-01},

journal = {Systems & Control Letters},

volume = {57},

number = {3},

pages = {209-215},

abstract = {We provide a cooperative control algorithm to stabilize symmetric formations to motion around closed curves suitable for mobile sensor networks. This work extends previous results for stabilization of symmetric circular formations. We study a planar particle model with decentralized steering control subject to limited communication. Because of their unique spectral properties, the Laplacian matrices of circulant graphs play a key role. We illustrate the result for a skewed superellipse, which is a type of curve that includes circles, ellipses, and rounded parallelograms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}