Treffer: Robot swarm aggregation using an improved Beeclust method
Title:
Robot swarm aggregation using an improved Beeclust method
Publisher Information:
International Journal of Intelligent Robotics and Applications (2025)
Publication Year:
2025
Subject Terms:
Robotics, Beeclust, Aggregation, Robot swarm, Optimization, Swarm robotics, Robot kinematics, Algorithms -- Simulation methods, Computational intelligence, MATLAB (Computer program language), Robótica de enjambre, Cinemática de robots, Algoritmos -- Métodos de simulación, Inteligencia computacional, MATLAB (Lenguaje de programación para computador)
Document Type:
Fachzeitschrift
article in journal/newspaper
File Description:
11 páginas; application/pdf
Language:
English
Relation:
Acevedo, O., Rios, Y.Y., Duque, J., Gomez, E., García, L.: A software for simulating robot swarm aggregation. In: FigueroaGarcía, J.C., Franco, C., Díaz-Gutierrez, Y., Hernández-Pérez, G. (eds.) Applied Computer Sciences in Engineering, pp. 386–399. Springer, Cham (2022a); Acevedo, O., Rios, Y.Y., García, L., Narvaez, D.: A study of the Beeclust algorithm for robot swarm aggregation. In: 2022 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT), pp. 1–6 (2022b). https://doi.org/10. 1109/ICMLANT56191.2022.9996514; Akopov, A.S., Beklaryan, L.A., Beklaryan, A.L.: Cluster-based optimization of an evacuation process using a parallel bi-objective real-coded genetic algorithm. Cybern. Inf. Technol. 20(3), 45–63 (2020); Al-Obaidy, M., Al-Azawi, R.: Cluster-based algorithm for energy optimization of swarmed robots using swarm intelligence. In: 2019 Sixth HCT Information Technology Trends (ITT), pp. 202–207. IEEE (2019); Amjadi, A.S., Raoufi, M., Turgut, A.E.: A self-adaptive landmarkbased aggregation method for robot swarms. Adapt. Behav. (2021). https://doi.org/10.1177/1059712320985543; Arvin, F., Samsudin, K., Ramli, A.R., Bekravi, M.: Imitation of honeybee aggregation with collective behavior of swarm robots. Int. J. Comput. Intell. Syst. 4, 739–748 (2012). https://doi.org/10.1080/ 18756891.2011.9727825; Arvin, F., Turgut, A.E., Bazyari, F., Arikan, K.B., Bellotto, N., Yue, S.: Cue-based aggregation with a mobile robot swarm: a novel fuzzy-based method. Adapt. Behav. 22, 189–206 (2014). https:// doi.org/10.1177/1059712314528009; Arvin, F., Turgut, A.E., Krajnik, T., Rahimi, S., Okay, I.E., Yue, S., Watson, S., Lennox, B.: Phi Clust: pheromone-based aggregation for robotic swarms. In: IEEE International Conference on Intelligent Robots and Systems, pp. 4288–4294 (2018). https://doi.org/ 10.1109/IROS.2018.8593961; Bayindir, L.: A review of swarm robotics tasks. Neurocomputing 172, 292–321 (2016). https://doi.org/10.1016/j.neucom.2015.05.116; Bodi, M., Thenius, R., Szopek, M., Schmickl, T., Crailsheim, K.: Interaction of robot swarms using the honeybee-inspired control algorithm BEECLUST. Math. Comput. Model. Dyn. Syst. 18, 87–100 (2012). https://doi.org/10.1080/13873954.2011.601420; Duarte, M., Costa, V., Gomes, J., Rodrigues, T., Silva, F., Oliveira, S.M., Christensen, A.L.: Evolution of collective behaviors for a real swarm of aquatic surface robots. PLoS One 11(3), 0151834 (2016); Hamann, H.: Swarm robotics: a formal approach. Swarm Robotics: A Formal Approach (2018). https://doi.org/10.1007/ 978-3-319-74528-2; Hamann, H., Schmickl, T., Wörn, H., Crailsheim, K.: Analysis of emergent symmetry breaking in collective decision making. Neural Comput. Appl. 21, 207–218 (2012); Hamann, H., Wörn, H., Crailsheim, K., Schmickl, T.: Spatial macroscopic models of a bio-inspired robotic swarm algorithm. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pp. 1415–1420 (2008). https://doi.org/10. 1109/IROS.2008.4651038; Kernbach, S., Thenius, R., Kernbach, O., Schmickl, T.: Re-embodiment of honeybee aggregation behavior in an artificial micro-robotic system. Adapt. Behav. 17, 237–259 (2009). https://doi.org/10. 1177/1059712309104966; Ramroop, S., Arvin, F., Watson, S., Carrasco-Gomez, J., Lennox, B.: A bio-inspired aggregation with robot swarm using real and simulated mobile robots. In: Giuliani, M., Assaf, T., Giannaccini, M.E. (eds.) Towards Autonomous Robotic Systems, pp. 317–329. Springer, Cham (2018); Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: Swarm Robotics, pp. 10–20. Springer, Berlin, Heidelberg (2005); Schmickl, T., Hamann, H., Worn, H., Crailsheim, K.: Two different approaches to a macroscopic model of a bio-inspired robotic swarm. Robot. Autonom. Syst. 57, 913–921 (2009a). https://doi. org/10.1016/J.ROBOT.2009.06.002; Schmickl, T., Thenius, R., Moeslinger, C., Radspieler, G., Kernbach, S., Szymanski, M., Crailsheim, K.: Get in touch: cooperative decision making based on robot-to-robot collisions. Autonom. Agents Multi-Agent Syst. 18(1), 133–155 (2009b); https://hdl.handle.net/20.500.12585/14190
DOI:
10.1007/s41315-025-00442-6
Rights:
https://creativecommons.org/licenses/by-nc-nd/4.0/ ; Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
Accession Number:
edsbas.D75EBC49
Database:
BASE
Weitere Informationen
Contiene ilustraciones, fotografías ; Swarm robotics is a topic within multi-robot systems that aims to solve engineering problems by drawing inspiration from the behavior observed in nature by social animals such as ants, bees, fish school, among others. The main challenge in these systems is the design of the controller, which must operate at the level of the individual robot to achieve results at the level of the entire swarm. In previous works, various basic behaviors of social insects have been studied and classified, which have been adapted to the field of robotics to emulate and use in the implementation of controllers and problem-solving. Examples of these behaviors include aggregation, dispersion, and resource searching (foraging). In this project, a simulation implemented in Matlab is presented, which implements a modified version of the Beeclust algorithm, which emulates the aggregation behavior of newly born bees. The modifications made focus on two basic actions of the algorithm: linear velocity and rotation angle. These adjustments have resulted in a 30% improvement in the average aggregation time compared to the original algorithm.