Vol. 18 Núm. 1 (2021): Revista Tekhnê



Review of flocking organization strategies for robot swarms

Revisión de las estrategias de organización en bandadas para enjambres de robots


  • Fredy H. Martínez S. Universidad Distrital Francisco José de Caldas

Palabras clave:

Emergence, flocking, multi-agent systems, path planning, swarm systems (en).

Palabras clave:

Bandada, emergencia, planificación de trayectorias, sistemas de enjambre, sistemas multiagentes (es).

Resumen (en)

Robotics promises great benefits for human beings, both at the industrial level and concerning personal services. This has led to the continuous development and research in different problems, including control, manipulation, human-machine interaction, and of course, autonomous navigation. Robot swarm systems promise an alternative solution to the classic high-performance platforms, particularly in applications that require task distribution. Among these systems, flocking navigation schemes are currently attracting high attention. To establish a frame of reference, a general review of the literature to date related to flocking behavior, in particular, optimized schemes with some guarantee of safety, is presented. In most of the cases presented, the characteristics of these systems, such as minimal computational and communication requirements, and event-driven planning, are maintained.

Resumen (es)

La robótica promete grandes beneficios, tanto a nivel industrial como con respecto a servicios personales. Esto ha incidido en el continuo desarrollo e investigación en diferentes problemas, entre ellos el control, la manipulación, la interacción hombre-máquina, y por supuesto, la navegación autónoma. Los sistemas de enjambres de robots prometen una alternativa de solución frente a las clásicas plataformas de alto de desempeño, particularmente en aplicaciones que requieren distribución de tareas. Entre estos sistemas, llama la atención los esquemas de navegación en bandada, los cuales tiene actualmente una alta atención. Para establecer un marco de referencia, se presenta una revisión general de la literatura a la fecha relacionada con comportamientos en bandada, en particular esquemas optimizados y con alguna garantía de seguridad. En la mayoría de los casos presentados se mantienen las características de estos sistemas, como son requisitos mínimos de computación y comunicación, y la planificación basada en eventos.


Alam, T., Reis, G. M., Bobadilla, L., & Smith, R. N. A data-driven deployment approach for persistent monitoring in aquatic environments. In: 2018 second IEEE international conference on robotic computing (IRC). 2018, 1–6.

Barve, A., & Nene, M. (2013). Survey of flocking algorithms in multi-agent systems. IJCSI International Journal of Computer Science Issues, 10(6), 110–117.

Bayındır, L. (2016). A review of swarm robotics tasks. Neurocomputing, 172, 292–321.

Beaumont, F., Taïar, R., & Polidori, G. (2017). Preliminary numerical investigation in open currents-water swimming: Pressure field in the swimmer wake. Applied Mathematics and Computation, 302(1), 48–57.

Beaver, L., Chalaki, B., Mahbub, A., Zhao, L., Zayas, R., & Malikopoulos, A. (2020). Demonstration of a time-efficient mobility system using a scaled smart city. Vehicle System Dynamics, 58(5), 787–804.

Beaver, L., & Malikopoulos, A. (2021). An overview on optimal flocking. Annual Reviews in Control, 51, 88–99.

Bedruz, R. A. R., Maningo, J. M. Z., Fernando, A. H., Bandala, A. A., Vicerra, R. R. P., & Dadios, E. P. Dynamic peloton formation configuration algorithm of swarm robots for aerodynamic effects optimization. In: 2019 7th international conference on robot intelligence technology and applications (RiTA). 2019, 264–267.

Bobadilla, L., Martinez, F., Gobst, E., Gossman, K., & LaValle, S. Controlling wild mobile robots using virtual gates and discrete transitions. In: 2012 american control conference (ACC). IEEE, 2012, 1–8.

Camperi, M., Cavagna, A., Giardina, I., Parisi, G., & Silvestri, E. (2012). Spatially balanced topological interaction grants optimal cohesion in flocking models. Interface Focus, 2(6), 715–725.

Celikkanat, H. Optimization of felf-organized flocking of a robot swarm via evolutionary strategies. In: 2008 23rd international symposium on computer and information sciences. 2008, 1–6.

Dave, A., & Malikopoulos, A. A. (2019). Decentralized stochastic control in partially nested information structures. IFAC-PapersOnLine, 52(20), 97–102.

Dave, A., & Malikopoulos, A. A. Structural results for decentralized stochastic control with a word-of-mouth communication. In: 2020 american control conference (ACC). 2020, 1–6.

Egerstedt, M., Pauli, J. N., Notomista, G., & Hutchinson, S. (2018). Robot ecology: Constraint-based control design for long duration autonomy. Annual Reviews in Control, 46, 1–7.

Fine, B., & Shell, D. (2013). Unifying microscopic flocking motion models for virtual, robotic, and biological flock members. Autonomous Robots, 35(2-3), 195–219.

Gatt, M. C., Quetting, M., Cheng, Y., & Wikelski, M. (2020). Dynamic body acceleration increases by 20% during flight ontogeny of greylag geese anser anser. Journal of Avian Biology, 51(2), e02235.

Genter, K. (2017). Fly with me : Algorithms and methods for influencing a flock (Doctoral dissertation). The University of Texas at Austin.

Hayes, A., & Dormiani-Tabatabaei, P. Self-organized flocking with agent failure: Off-line optimization and demonstration with real robots. In: Proceedings 2002 IEEE international conference on robotics and automation (cat. no.02ch37292). 2002, 1–6.

Ibuki, T., Wilson, S., Yamauchi, J., Fujita, M., & Egerstedt, M. (2020). Optimization-based distributed flocking control for multiple rigid bodies. IEEE Robotics and Automation Letters, 5(2), 1891–1898.

Jafari, M., Xu, H., & Carrillo, L. R. G. (2020). A biologically-inspired reinforcement learning based intelligent distributed flocking control for multi-agent systems in presence of uncertain system and dynamic environment. IFAC Journal of Systems and Control, 13, 100096.

Kölzsch, A., Flack, A., Müskens, G. J. D. M., Kruckenberg, H., Glazov, P., & Wikelski, M. (2020). Goose parents lead migration v. Journal of Avian Biology, 51(3), e02392.

La, H. M., Lim, R., & Sheng, W. (2015). Multirobot cooperative learning for predator avoidance. IEEE Transactions on Control Systems Technology, 23(1), 52–63.

La, H. M., Nguyen, T. H., Nguyen, C. H., & Nguyen, H. N. Optimal flocking control for a mobile sensor network based a moving target tracking. In: 2009 IEEE international conference on systems, man and cybernetics. 2009, 1–6.

Makiguchi, M., & Inoue, J. Anisotropy measurement in artificial flockings: How does one design the optimal boids by evolutionary computation? In: 1st international conference on soft computing and intelligent systems and 11th international symposium on advanced intelligent systems. 2010, 1–6.

Martínez, F., & Delgado, J. (2012). Wireless visual sensor network robots - based for the emulation of collective behavior. Tecnura, 16(31), 10–18.

Martínez, F., Jacinto, E., & Acero, D. Brownian motion as exploration strategy for autonomous swarm robots. In: 2012 IEEE international conference on robotics and biomimetics (ROBIO). 2012, 2375–2380.

Martínez, F., Rendón, A., & Arbulú, M. (2018). An algorithm based on the bacterial swarm and its application in autonomous navigation problems. Lecture notes in computer science (pp. 304–313). Springer International Publishing.

Mirzaeinia, A., Hassanalian, M., Lee, K., & Mirzaeinia, M. (2019). Energy conservation of v-shaped swarming fixed-wing drones through position reconfiguration. Aerospace Science and Technology, 94(1), 105398.

Morgan, D., Subramanian, G. P., Chung, S.-J., & Hadaegh, F. Y. (2016). Swarm assignment and trajectory optimization using variable-swarm, distributed auction assignment and sequential convex programming. The International Journal of Robotics Research, 35(10), 1261–1285.

Morihiro, K., Isokawa, T., Nishimura, H., & Matsui, N. Characteristics of flocking behavior model by reinforcement learning scheme. In: 2006 SICE-ICASE international joint conference. 2006, 1–6.

Morihiro, K., Isokawa, T., Nishimura, H., & Matsui, N. Emergence of flocking behavior based on reinforcement learning. In: Lecture notes in computer science. 2006, 699–706.

Nathan, A., & Barbosa, V. C. (2008). V-like formations in flocks of artificial birds. Artificial Life, 14(2), 179–188.

Navarro, I., Mario, E. D., & Martino, A. Distributed vs. centralized particle swarm optimization for learning flocking behaviors. In: European conference on artificial life 2015. 2015, 1–6.

Nayyar, A., Mahajan, A., & Teneketzis, D. (2013). Decentralized stochastic control with partial history sharing: A common information approach. IEEE Transactions on Automatic Control, 58(7), 1644–1658.

Oh, H., Shirazi, A., Sun, C., & Jin, Y. (2017). Bio-inspired self-organising multi-robot pattern formation: A review. Robotics and Autonomous Systems, 91, 83–100.

Olfati-Saber, R. (2006). Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Transactions on Automatic Control, 51(3), 401–420.

Ouvrard, T., Groslambert, A., Ravier, G., Grosprêtre, S., Gimenez, P., & Grappe, F. (2018). Mechanisms of performance improvements due to a leading teammate during uphill cycling. International Journal of Sports Physiology and Performance, 13(9), 1215–1222.

Qiu, H., & Duan, H. (2020). A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles. Information Sciences, 509, 515–529.

Reynolds, C. (1987). Flocks, herds and schools: A distributed behavioral model. Computer Graphics, 24(1), 25–34.

Sankey, D., & Portugal, S. (2019). When flocking is costly: Reduced cluster-flock density over long-duration flight in pigeons. The Science of Nature, 106(7-8), 1–5.

Semnani, S., & Basir, O. (2017). Multi-target engagement in complex mobile surveillance sensor networks. Unmanned Systems, 05(01), 31–43.

Song, Z., Lipinski, D., & Mohseni, K. (2017). Multi-vehicle cooperation and nearly fuel-optimal flock guidance in strong background flows. Ocean Engineering, 141(1), 388–404.

Vásárhelyi, G., Virágh, C., Somorjai, G., Nepusz, T., Eiben, A. E., & Vicsek, T. (2018). Optimized flocking of autonomous drones in confined environments. Science Robotics, 3(20), 1–6.

Vatankhah, R., Etemadi, S., Honarvar, M., Alasty, A., Boroushaki, M., & Vossoughi, G. Online velocity optimization of robotic swarm flocking using particle swarm optimization (PSO) method. In: 2009 6th international symposium on mechatronics and its applications. 2009, 1–6.

Veitch, C., Render, D., & Aravind, A. Ergodic flocking. In: 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS). 2019, 1–6.

Wang, C., Wang, J., & Zhang, X. A Deep Reinforcement Learning Approach To Flocking And Navigation Of Uavs In Large-Scale Complex Environments. In: 2018 IEEE global conference on signal and information processing (GlobalSIP). 2018, 1–6.

Wang, W., Zheng, Y., Lin, G., Zhang, L., & Han, Z. (2020). Swarm robotics: A review. Jiqiren/Robot, 42(2), 232–256.

Wilson, S., Glotfelter, P., Wang, L., Mayya, S., Notomista, G., Mote, M., & Egerstedt, M. (2020). The robotarium: Globally impactful opportunities, challenges, and lessons learned in remote-access, distributed control of multirobot systems. IEEE Control Systems, 40(1), 26–44.

Xiao, Y., Zhang, L., Jin, X., & Kong, Z. (2018). Event-triggered flocking control of mas and its application in maintaining of power line communication system. IPPTA: Quarterly Journal of Indian Pulp and Paper Technical Association, 30(4), 231–240.

Xu, H., & Carrillo, L. R. G. (2017). Fast reinforcement learning based distributed optimal flocking control and network co-design for uncertain networked multi-UAV system (R. E. Karlsen, D. W. Gage, C. M. Shoemaker, & H. G. Nguyen, Eds.). Unmanned Systems Technology XIX, 10195(1), 1–6.

Yang, J., Wang, X., & Bauer, P. (2018). Line and v-shape formation based distributed processing for robotic swarms. Sensors, 18(8), 2543.

Yang, J., Grosu, R., Smolka, S. A., & Tiwari, A. Love thy neighbor: V-formation as a problem of model predictive control (invited paper). In: 27th international conference on concurrency theory (concur 2016). 2016, 4:1–4:5. https:// 10.4230/LIPIcs.CONCUR.2016.4.

Yuan, Q., Zhan, J., & Li, X. (2017). Outdoor flocking of quadcopter drones with decentralized model predictive control. ISA Transactions, 71, 84–92.

Yuan, W., Ganganath, N., Cheng, C., Qing, G., Lau, F., & Zhao, Y. (2020). Path-planning-enabled semiflocking control for multitarget monitoring in mobile sensor networks. IEEE Transactions on Industrial Informatics, 16(7), 4778–4787.

Zhang, H.-T., Chen, M., Stan, G.-B., Zhou, T., & Maciejowski, J. (2008). Collective behavior coordination with predictive mechanisms. IEEE Circuits and Systems Magazine, 8(3), 67–85.

Zhu, B., Xie, L., & Han, D. Recent developments in control and optimization of swarm systems: A brief survey. In: 2016 12th IEEE international conference on control and automation (ICCA). 2016, 1–6.

Cómo citar


Martínez S., F. H. (2021). Review of flocking organization strategies for robot swarms. Tekhnê, 18(1), 13–20.


Martínez S., F.H. 2021. Review of flocking organization strategies for robot swarms. Tekhnê. 18, 1 (jun. 2021), 13–20.


Martínez S., F. H. Review of flocking organization strategies for robot swarms. Tekhnê 2021, 18, 13-20.


MARTÍNEZ S., Fredy H. Review of flocking organization strategies for robot swarms. Tekhnê, [S. l.], v. 18, n. 1, p. 13–20, 2021. Disponível em: Acesso em: 23 abr. 2024.


Martínez S., Fredy H. 2021. «Review of flocking organization strategies for robot swarms». Tekhnê 18 (1):13-20.


Martínez S., F. H. (2021) «Review of flocking organization strategies for robot swarms», Tekhnê, 18(1), pp. 13–20. Disponible en: (Accedido: 23 abril 2024).


F. H. Martínez S., «Review of flocking organization strategies for robot swarms», Tekhnê, vol. 18, n.º 1, pp. 13–20, jun. 2021.


Martínez S., Fredy H. «Review of flocking organization strategies for robot swarms». Tekhnê, vol. 18, n.º 1, junio de 2021, pp. 13-20,


Martínez S., Fredy H. «Review of flocking organization strategies for robot swarms». Tekhnê 18, no. 1 (junio 25, 2021): 13–20. Accedido abril 23, 2024.


Martínez S. FH. Review of flocking organization strategies for robot swarms. Tekhnê [Internet]. 25 de junio de 2021 [citado 23 de abril de 2024];18(1):13-20. Disponible en:

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