IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v637y2024ics0378437124001092.html
   My bibliography  Save this article

Anticipation promotes the velocity alignment in collective motion

Author

Listed:
  • Zhou, Yongjian
  • Wang, Tao
  • Wang, Tonghao
  • Lei, Xiaokang
  • Peng, Xingguang

Abstract

Anticipation is a fundamental capacity found in many living organisms, particularly in humans. In this study, we investigate the influence of anticipation on collective behavior based on a self-propelled model that relies on solely attractive interaction forces. Focal particles employ anticipation to predict the future positions of neighboring particles, foreseeing their locations τ time steps ahead by utilizing linear extrapolation based on adjacent time step positions. The results demonstrate that our model can attain velocity alignment from random initial states. Theoretical derivations further reveal that as both τ and the interaction amplitude among particles increase, the translation velocity of the swarm accelerates. Notably, under certain parameters, noise above a certain value can drive swarm from misaligned to the velocity alignment state. These insights have been validated through both particle simulations and experiments employing swarm robots. Our work reveals the significant role of anticipation in collective behavior and provides a novel approach for achieving velocity alignment in swarm.

Suggested Citation

  • Zhou, Yongjian & Wang, Tao & Wang, Tonghao & Lei, Xiaokang & Peng, Xingguang, 2024. "Anticipation promotes the velocity alignment in collective motion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    • Handle: RePEc:eee:phsmap:v:637:y:2024:i:c:s0378437124001092
    DOI: 10.1016/j.physa.2024.129601
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437124001092
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2024.129601?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Shuai & Lei, Xiaokang & Zheng, Zhicheng & Peng, Xingguang, 2022. "Collective fission behavior in swarming systems with density-based interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    2. Becco, Ch. & Vandewalle, N. & Delcourt, J. & Poncin, P., 2006. "Experimental evidences of a structural and dynamical transition in fish school," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 487-493.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sánchez, R. & Díaz-Leyva, P., 2018. "Self-assembly and speed distributions of active granular particles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 11-19.
    2. Zhu, Jian & Xi, Jingke & Li, Shihan & Shi, Hongjun & Sun, Yongzheng, 2024. "Time cost estimation for flocking of Cucker–Smale type models with switching protocol," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).
    3. Šćepanović, J.R. & Karač, A. & Jakšić, Z.M. & Budinski-Petković, Lj. & Vrhovac, S.B., 2019. "Group chase and escape in the presence of obstacles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 450-465.
    4. Richard P Mann & Andrea Perna & Daniel Strömbom & Roman Garnett & James E Herbert-Read & David J T Sumpter & Ashley J W Ward, 2012. "Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection," PLOS Computational Biology, Public Library of Science, vol. 8(1), pages 1-12, January.
    5. Richard P Mann & Andrea Perna & Daniel Strömbom & Roman Garnett & James E Herbert-Read & David J T Sumpter & Ashley J W Ward, 2013. "Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-13, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:637:y:2024:i:c:s0378437124001092. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.