Relevant bibliographies by topics / Flocking behavior

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Flocking behavior'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Flocking behavior"

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Yang, Panpan, Maode Yan, Jiacheng Song, and Ye Tang. "Self-Organized Fission-Fusion Control Algorithm for Flocking Systems Based on Intermittent Selective Interaction." Complexity 2019 (February 11, 2019): 1–12. http://dx.doi.org/10.1155/2019/2187812.

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In nature, gregarious animals, insects, or bacteria usually exhibit paradoxical behaviors in the form of group fission and fusion, which exerts an important influence on group’s pattern formation, information transfer, and epidemiology. However, the fission-fusion dynamics have received little attention compared to other flocking behavior. In this paper, an intermittent selective interaction based control algorithm for the self-organized fission-fusion behavior of flocking system is proposed, which bridges the gap between the two conflicting behaviors in a unified fashion. Specifically, a hybr
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Tan, Changhui. "A discontinuous Galerkin method on kinetic flocking models." Mathematical Models and Methods in Applied Sciences 27, no. 07 (2017): 1199–221. http://dx.doi.org/10.1142/s0218202517400139.

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We study kinetic representations of flocking models. They arise from agent-based models for self-organized dynamics, such as Cucker–Smale [Emergent behaviors in flocks, IEEE Trans. Autom. Control. 52 (2007) 852–862] and Motsch–Tadmor [A new model for self-organized dynamics and its flocking behavior, J. Statist. Phys. 144 (2011) 923–947] models. We first establish a well-posedness theory and large-time flocking behavior for the kinetic systems, which indicates a concentration in velocity variable in infinite time. We then apply a discontinuous Galerkin method to treat the asymptotic [Formula:
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Al-Tawil, Marwan, Moh’d Belal Al-Zoubi, Omar Y. Adwan, Ammar Al-Huneiti, and Reem Q. Al Fayez. "Efficient method for finding nearest neighbors in flocking behaviors using k-dimensional trees." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1628. http://dx.doi.org/10.11591/ijai.v12.i4.pp1628-1635.

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<p>Flocking is a behavior where a group of objects travel, move or collaborate together. By learning more about flocking behavior, we might be able to apply this knowledge in different contexts such as computer graphics, games, and education. A key steppingstone for understanding flocking behavior is to be able to simulate it. However, simulating behaviors of large numbers of objects is highly compute-intensive task because of the n-squared complexity of nearest neighbor for separating n objects. The work in this paper presents an efficient nearest neighbor method based on the k-dimensio
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Al-Tawil, Marwan, Moh'd Belal Al-Zoubi, Omar Y. Adwan, Ammar Al-Huneiti, and Fayez Reem Q. Al. "Efficient method for finding nearest neighbors in flocking behaviors using k-dimensional trees." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1628–35. https://doi.org/10.11591/ijai.v12.i4.pp1628-1635.

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Flocking is a behavior where a group of objects travel, move or collaborate together. By learning more about flocking behavior, we might be able to apply this knowledge in different contexts such as computer graphics, games, and education. A key steppingstone for understanding flocking behavior is to be able to simulate it. However, simulating behaviors of large numbers of objects is highly compute-intensive task because of the n-squared complexity of nearest neighbor for separating n objects. The work in this paper presents an efficient nearest neighbor method based on the k-dimensional trees
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Pomara, Lars Y., Robert J. Cooper, and Lisa J. Petit. "Mixed-Species Flocking and Foraging Behavior of Four Neotropical Warblers in Panamanian Shade Coffee Fields and Forests." Auk 120, no. 4 (2003): 1000–1012. http://dx.doi.org/10.1093/auk/120.4.1000.

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Abstract We quantified foraging rates and foraging visibility metrics for four Neotropical warblers—Slate-throated Redstart (Myoborus miniatus), Golden-crowned Warbler (Basileuterus culicivorus), Wilson's Warbler (Wilsonia pusilla), and Black-and-White Warbler (Mniotilta varia)—under flocking and solitary conditions in western Panama to test hypotheses regarding the relative influences of predation pressure and social facilitation on foraging behavior. We also compared foraging behavior in primary forests and in traditionally managed shade coffee fields for two species (Slate-throated Redstart
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Wang, Xiaojian. "IoT-based simulation of flocking algorithm." Journal of Physics: Conference Series 2580, no. 1 (2023): 012013. http://dx.doi.org/10.1088/1742-6596/2580/1/012013.

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Abstract The study of animal groups’ flocking behavior is of great significance to the study of multi-agent system. Flocking algorithm is a typical algorithm for group control based on animal group behavior. The research on flocking algorithm is mature, but there is little research on the implementation of Flocking algorithm using digital twin. Digital twins, as a key technology in the direction of IoT, is potentially valuable for improving the efficiency of cloud and devices. Therefore, the main research goal of this paper is to simulate the flocking phenomenon in nature by using digital twin
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Masehian, Ellips, and Mitra Royan. "Characteristics of and Approaches to Flocking in Swarm Robotics." Applied Mechanics and Materials 841 (June 2016): 240–49. http://dx.doi.org/10.4028/www.scientific.net/amm.841.240.

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One of the basic collective behaviors of swarm robotic systems is flocking, which has been actively studied for more than a decade and mimics a natural phenomenon where a group of animals move together as a single entity. In flocking, each robot in the flock tries to adjust its velocity and align with other robots in the flock while maintaining a predetermined pattern (formation) and avoiding collisions with obstacle and other members of the flock. This paper presents an up-to-date review on the characteristics of flocking problems and tasks, as well as solution approaches to flocking problems
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ITOH, MAKOTO, and LEON O. CHUA. "BOIDS CONTROL OF CHAOS." International Journal of Bifurcation and Chaos 17, no. 02 (2007): 427–44. http://dx.doi.org/10.1142/s0218127407017525.

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Chaotic nonlinear networks are investigated, which are controlled by simple boids rules. They exhibit complex and emergent behaviors such as flocking behavior, separation behavior, joining behavior and obstacle avoiding behavior.
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Oweis, Sami, Subramaniam Ganesan, and Ka C. Cheok. "Illustration of Centralized Command and Control for Flocking Behavior." International Journal of Handheld Computing Research 5, no. 2 (2014): 1–22. http://dx.doi.org/10.4018/ijhcr.2014040101.

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Flocking is a term that describes the behavior of a group of birds (a “flock”) in flight, or the swarming behavior of insects. This paper presents detailed information about how to use the flocking techniques to control a group of embedded controlled systems - ‘'Boids''- such as ground systems (robotic vehicles/ swarm robots). Each one of these systems collectively moves inside/outside of a building to reach a target. The flocking behavior is implemented on a server-based control, which processes each of the boids' properties e.g. position, speed & target. Subsequently, the server will ass
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Yamauchi, Sho, Hidenori Kawamura, and Keiji Suzuki. "Observation of Synchronization Phenomena in Structured Flocking Behavior." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 5 (2013): 715–20. http://dx.doi.org/10.20965/jaciii.2013.p0715.

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Flocking algorithms for multi-agent systems are distributed algorithms that generate complex formational movement despite having simple rules for each agent. These algorithms, known as swarmintelligence, are flexible and robust. However, to exploit these features to generate flexible behavior in an autonomous system, greater flexibility is needed. To achieve this, these algorithms are extended to enable arbitrary lattice formation. In addition, extended flocking algorithms can be assumed to be the aggregation of oscillators and observed the behavior of synchronization. It is difficult to expla
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Dissertations / Theses on the topic "Flocking behavior"

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Blomqvist, Oscar, Sebastian Bremberg, and Richard Zauer. "Mathematical modeling of flocking behavior." Thesis, KTH, Optimeringslära och systemteori, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-103812.

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In this thesis, the ocking behaviour of prey when threatened by a group of predators, is investigated using dynamical systems. By implementing the unicycle model, a simulation is created using Simulink and Matlab. A set of forces are set up to describe the state of the prey, that in turn determines their behaviour in dierent scenarios. An eective strategy is found so all members of the ock can survive the predator attack, taking into account the advantages of the predator's greater translational velocity and the prey's higher angular velocity. Multiple obstacles and an energy constraint are ad
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Gueleri, Roberto Alves. "Desenvolvimento de técnicas de aprendizado de máquina via sistemas dinâmicos coletivos." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-13112017-111652/.

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O aprendizado de máquina consiste em conceitos e técnicas que permitem aos computadores melhorar seu desempenho com a experiência, ou em outras palavras, aprender com dados. Duas de suas principais categorias são o aprendizado não-supervisionado e o semissupervisionado, que respectivamente consistem em inferir padrões em bases cujos dados não têm rótulo (classe) e classificar dados em bases parcialmente rotuladas. Embora muito estudado, trata-se de um campo repleto de desafios e com muitos tópicos abertos. Sistemas dinâmicos coletivos, por sua vez, são sistemas constituídos por muitos indivídu
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Gueleri, Roberto Alves. "Agrupamento de dados baseado em comportamento coletivo e auto-organização." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-29072013-164559/.

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O aprendizado de máquina consiste de conceitos e técnicas que permitem aos computadores melhorar seu desempenho com a experiência, ou, em outras palavras, aprender com dados. Um dos principais tópicos do aprendizado de máquina é o agrupamento de dados que, como o nome sugere, procura agrupar os dados de acordo com sua similaridade. Apesar de sua definição relativamente simples, o agrupamento é uma tarefa computacionalmente complexa, tornando proibitivo o emprego de algoritmos exaustivos, na busca pela solução ótima do problema. A importância do agrupamento de dados, aliada aos seus desafios, f
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Roberts, J. G. "Studies of the flocking behaviour of Sanderlings, Calidris alba." Thesis, Durham University, 1990. http://etheses.dur.ac.uk/6248/.

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The behaviour of sanderlings, Calidris alba, was studied at Teesmouth, N. E. England. Flock sizes and within-flock spacing were related to factors such as substrate type, season, and prey distribution. Sightings of individually-identifiable colour-ringed birds showed that turnover was high. Individuals varied in their vagility and in the way in which they allocated their time between feeding sites. There was a high degree of consistency between winters in the distributions of individual birds. When individual sighting frequencies were taken into account, associations between individual sanderl
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Henderson, Jane Victoria. "Flocking behaviour of ducks in response to predator stimuli." Thesis, University of Bristol, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284861.

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Hmidi, Mehdi. "Formation Control and UAV Path Finding Under Uncertainty : A contingent and cooperative swarm intelligence approach." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-91379.

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Several of our technological breakthroughs are influenced by types of behavior and structures developed in the natural world, including the emulation of swarm in- telligence and the engineering of artificial synapses that function like the human mind. Much like these breakthroughs, this report examines emerging behaviors across swarms of non-communicating, adaptive units that evade obstacles while find- ing a path, to present a swarming algorithm premised on a class of local rule sets re- sulting in a Unmanned Aerial Vehicle (UAV) group navigating together as a unified swarm. Primarily, this m
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Chang, Gabriel, and Michaela Stjerndal. "Investigating and Modeling the Emergent Flocking Behaviour of Sheep Under Threat with Fear Contagion." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260246.

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Virtual environments can feel lifeless or robotic, and a way to combat this is the inclusion of living creatures with life-like behaviour. Wanting to bring this into computer animation, Craig W. Reynolds formulated a model to simulate the flocking behaviour of birds. Delgado-Mata made extensions upon Reynolds’ flocking model, and his model has in turn been used to simulate a sheep herding scenario. The herding scenario can extend into many peripheral fields, where an effective strategy for a single agent to maneuver a large group of unwilling agents can have many applications. This study inves
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Li, Martin. "Experimental study of swimming flagellated bacteria and their collective behaviour in concentrated suspensions." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4787.

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This thesis investigates bacterial motility from the mechanism permitting individual selfpropulsion to the complex collective flocking motility in Escherichia coli and Bacillus subtilis cells. Understanding bacterial swimming has intrigued scientists for decades and recently there has been a growing interest in collective swimming behaviour. The first part of this thesis reviews the characteristics of E. coli and B. subtilis cells subsequently describing the governing physics and constraints of self-propulsion in the low Reynolds regime. The second part of this thesis presents three self-conta
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Ruini, Fabio. "Distributed control for collective behaviour in micro-unmanned aerial vehicles." Thesis, University of Plymouth, 2013. http://hdl.handle.net/10026.1/1549.

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The work presented herein focuses on the design of distributed autonomous controllers for collective behaviour of Micro-unmanned Aerial Vehicles (MAVs). Two alternative approaches to this topic are introduced: one based upon the Evolutionary Robotics (ER) paradigm, the other one upon flocking principles. Three computer simulators have been developed in order to carry out the required experiments, all of them having their focus on the modelling of fixed-wing aircraft flight dynamics. The employment of fixed-wing aircraft rather than the omni-directional robots typically employed in collective r
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Farine, Damien R. "Emergent social structure and collective behaviour from individual decision-making in wild birds." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:9c574271-1814-4fdc-9819-b457924ae816.

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Social behaviour is shaped by complex relationships between evolutionary and ecological processes interacting at different scales. Benefits gained from social associations can range from predator dilution to collective sensing, but little is known about how these can be influenced by social structure and phenotypic composition. In this thesis, I investigated how individual decision-making affects phenotypic social structure, and how this mediates social behaviour through emergent properties of collective group behaviour. First, using mixed-species flocks as a model system, I showed individual
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Books on the topic "Flocking behavior"

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Filipowicz, Judy Rachel. Using ART 1 (Adaptive Resonance Theory 1) to Study Flocking Behaviour in Intelligent agents. Oxford Brookes University, 2001.

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Sethna, James P. Statistical Mechanics: Entropy, Order Parameters, and Complexity. 2nd ed. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198865247.001.0001.

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This text distills the core ideas of statistical mechanics to make room for new advances important to information theory, complexity, active matter, and dynamical systems. Chapters address random walks, equilibrium systems, entropy, free energies, quantum systems, calculation and computation, order parameters and topological defects, correlations and linear response theory, and abrupt and continuous phase transitions. Exercises explore the enormous range of phenomena where statistical mechanics provides essential insight — from card shuffling to how cells avoid errors when copying DNA, from th
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Book chapters on the topic "Flocking behavior"

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Aoyagi, Masaru, and Akira Namatame. "Dynamics of Emergent Flocking Behavior." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11861201_64.

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Mehmood, Usama, Shouvik Roy, Radu Grosu, Scott A. Smolka, Scott D. Stoller, and Ashish Tiwari. "Neural Flocking: MPC-Based Supervised Learning of Flocking Controllers." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45231-5_1.

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AbstractWe show how a symmetric and fully distributed flocking controller can be synthesized using Deep Learning from a centralized flocking controller. Our approach is based on Supervised Learning, with the centralized controller providing the training data, in the form of trajectories of state-action pairs. We use Model Predictive Control (MPC) for the centralized controller, an approach that we have successfully demonstrated on flocking problems. MPC-based flocking controllers are high-performing but also computationally expensive. By learning a symmetric and distributed neural flocking con
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Aoyagi, Masaru, and Akira Namatame. "Incremental Development of Networked Intelligence in Flocking Behavior." In Emergent Intelligence of Networked Agents. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71075-2_1.

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Morihiro, Koichiro, Teijiro Isokawa, Haruhiko Nishimura, and Nobuyuki Matsui. "Emergence of Flocking Behavior Based on Reinforcement Learning." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893011_89.

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Cheng, Lei, Jun Wang, Huaiyu Wu, et al. "Flocking Behavior via Leader’s Backstepping on Nonholonomic Robot Group." In Applied Methods and Techniques for Mechatronic Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36385-6_5.

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Jung, Soon-Gyo, Sanggil Yeom, Min Han Shon, Dongsoo Stephen Kim, and Hyunseung Choo. "Clustering Wireless Sensor Networks Based on Bird Flocking Behavior." In Computational Science and Its Applications -- ICCSA 2015. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21410-8_10.

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Antoniou, Pavlos, Andreas Pitsillides, Andries Engelbrecht, Tim Blackwell, and Loizos Michael. "Congestion Control in Wireless Sensor Networks Based on the Bird Flocking Behavior." In Self-Organizing Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10865-5_21.

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Olfati-Saber, Reza. "Design of Behavior of Swarms: From Flocking to Data Fusion using Microfilter Networks." In Cooperative Control of Distributed Multi-Agent Systems. John Wiley & Sons, Ltd, 2007. http://dx.doi.org/10.1002/9780470724200.ch2.

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dell’Erba, Ramiro. "A Plausible Description of Continuum Material Behavior Derived by Swarm Robot Flocking Rules." In Advanced Structured Materials. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53755-5_18.

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Yang, Ming, and John Reif. "Social DNA Nanorobots." In Natural Computing Series. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9891-1_20.

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AbstractWe describe social DNA nanorobots, which are autonomous mobile DNA devices that execute a series of pair-wise interactions between simple individual DNA nanorobots, causing a desired overall outcome behavior for the group of nanorobots which can be relatively complex. We present various designs for social DNA nanorobots that walk over a 2D nanotrack and collectively exhibit various programmed behaviors. These employ only hybridization and strand-displacement reactions, without use of enzymes. The novel behaviors of social DNA nanorobots designed here include: (i) Self-avoiding random w
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Conference papers on the topic "Flocking behavior"

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Pita-Romero, Carmen, Pedro Arias-Perez, Miguel Fernandez-Cortizas, Rafael Perez-Segui, and Pascual Campoy. "Flocking Behavior for Dynamic and Complex Swarm Structures." In 2025 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2025. https://doi.org/10.1109/icuas65942.2025.11007905.

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Jin, Qiu, Deng Honggao, Ji Xiaoyu, and Fu Qiang. "Research on flocking behavior of multi-perturbation Cucker-Smale model." In 2024 IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). IEEE, 2024. https://doi.org/10.1109/iciba62489.2024.10867894.

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Stankovic, Srdjan S. "Swarming and flocking: Cooperative collective behavior." In 2008 9th Symposium on Neural Network Applications in Electrical Engineering (NEUREL 2008). IEEE, 2008. http://dx.doi.org/10.1109/neurel.2008.4685542.

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Li Xiang, M. Fikret Ercan, Zhou Yi, and Yu Fai Fung. "Algorithm for swarm robot flocking behavior." In 2009 4th International Conference on Autonomous Robots and Agents. IEEE, 2009. http://dx.doi.org/10.1109/icara.2000.4803943.

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Turgeman, Avi, Adwait Datar, and Herbert Werner. "Gradient Free Source-Seeking Using Flocking Behavior." In 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8815372.

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"Local information maximisation creates emergent flocking behavior." In ECAL 2011: The 11th European Conference on Artificial Life. MIT Press, 2011. http://dx.doi.org/10.7551/978-0-262-29714-1-ch104.

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Yang Zhou, Lin Wang, and Qi Chen. "Intervention of flocking behavior based on collision avoidance." In 2012 24th Chinese Control and Decision Conference (CCDC). IEEE, 2012. http://dx.doi.org/10.1109/ccdc.2012.6244091.

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Lee, Jae Moon, Se Hong Cho, and Rafael A. Calvo. "A fast algorithm for simulation of flocking behavior." In 2009 International IEEE Consumer Electronics Society's Games Innovations Conference (ICE-GIC 2009). IEEE, 2009. http://dx.doi.org/10.1109/icegic.2009.5293611.

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Colqui, Gissela, Mitsuhiro Tomita, Taizoh Hattori, and Yasutami Chigusa. "New video synthesis based on flocking behavior simulation." In 2008 3rd International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2008. http://dx.doi.org/10.1109/isccsp.2008.4537358.

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Morihiro, Koichiro, Teijiro Isokawa, Haruhiko Nishimura, and Nobuyuki Matsui. "Characteristics of Flocking Behavior Model by Reinforcement Learning Scheme." In 2006 SICE-ICASE International Joint Conference. IEEE, 2006. http://dx.doi.org/10.1109/sice.2006.315087.

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