A study of the Beeclust algorithm for robot swarm aggregation
Swarm robotics is a topic that has gained momentum in recent years thanks to its possibility to solve different engineering problems. Many robots are expected to work collaboratively to solve a given task. One of the main challenges is the design of the robot controller since it must be defined at t...
- Autores:
-
Acevedo, Oscar
Rios, Yuliana Y.
Garcia, Luis
Narvaez, Dayana
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12143
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12143
- Palabra clave:
- Swarm Robotics;
Swarm;
Social Insects
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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A study of the Beeclust algorithm for robot swarm aggregation |
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A study of the Beeclust algorithm for robot swarm aggregation |
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A study of the Beeclust algorithm for robot swarm aggregation Swarm Robotics; Swarm; Social Insects LEMB |
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A study of the Beeclust algorithm for robot swarm aggregation |
| title_full |
A study of the Beeclust algorithm for robot swarm aggregation |
| title_fullStr |
A study of the Beeclust algorithm for robot swarm aggregation |
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A study of the Beeclust algorithm for robot swarm aggregation |
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A study of the Beeclust algorithm for robot swarm aggregation |
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Acevedo, Oscar Rios, Yuliana Y. Garcia, Luis Narvaez, Dayana |
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Acevedo, Oscar Rios, Yuliana Y. Garcia, Luis Narvaez, Dayana |
| dc.subject.keywords.spa.fl_str_mv |
Swarm Robotics; Swarm; Social Insects |
| topic |
Swarm Robotics; Swarm; Social Insects LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
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Swarm robotics is a topic that has gained momentum in recent years thanks to its possibility to solve different engineering problems. Many robots are expected to work collaboratively to solve a given task. One of the main challenges is the design of the robot controller since it must be defined at the robot level to accomplish a task at the swarm level. The characteristics and properties of natural swarms have been studied to solve this problem. From these studies, basic behaviors have been defined, one of them being aggregation. This work explores the classical aggregation algorithm known as Beeclust. The Beeclust algorithm was implemented in MATLAB. Test were performed to determine its effectiveness in forming aggregates and the factors that affect its efficiency. © 2022 IEEE. |
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2022 |
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2022 |
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2023-07-18T19:32:25Z |
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2023-07-18T19:32:25Z |
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2023 |
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Acevedo, O., Rios, Y. Y., Garcia, L., & Narvaez, D. (2022). A study of the Beeclust algorithm for robot swarm aggregation. 2022 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT), 1–6. |
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https://hdl.handle.net/20.500.12585/12143 |
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10.1109/ICMLANT56191.2022.9996514 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Acevedo, O., Rios, Y. Y., Garcia, L., & Narvaez, D. (2022). A study of the Beeclust algorithm for robot swarm aggregation. 2022 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT), 1–6. 10.1109/ICMLANT56191.2022.9996514 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12143 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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6 páginas |
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application/pdf |
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Cartagena de Indias |
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IEEE International Conference on Machine Learning and Applied Network Technologies |
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Universidad Tecnológica de Bolívar |
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Acevedo, Oscar3da5ca49-950c-4cc5-83d5-97c2efb05371Rios, Yuliana Y.0dbdd108-59ee-4e22-85cd-3de4922f37b4Garcia, Luisfd9812a0-fa74-49df-93b6-48c75c199b55Narvaez, Dayana21b61675-57e5-4d0c-80c9-2d8e866d39622023-07-18T19:32:25Z2023-07-18T19:32:25Z20222023Acevedo, O., Rios, Y. Y., Garcia, L., & Narvaez, D. (2022). A study of the Beeclust algorithm for robot swarm aggregation. 2022 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT), 1–6.https://hdl.handle.net/20.500.12585/1214310.1109/ICMLANT56191.2022.9996514Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarSwarm robotics is a topic that has gained momentum in recent years thanks to its possibility to solve different engineering problems. Many robots are expected to work collaboratively to solve a given task. One of the main challenges is the design of the robot controller since it must be defined at the robot level to accomplish a task at the swarm level. The characteristics and properties of natural swarms have been studied to solve this problem. From these studies, basic behaviors have been defined, one of them being aggregation. This work explores the classical aggregation algorithm known as Beeclust. The Beeclust algorithm was implemented in MATLAB. Test were performed to determine its effectiveness in forming aggregates and the factors that affect its efficiency. © 2022 IEEE.6 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2IEEE International Conference on Machine Learning and Applied Network TechnologiesA study of the Beeclust algorithm for robot swarm aggregationinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Swarm Robotics;Swarm;Social InsectsLEMBCartagena de IndiasHamann, H. Swarm robotics: A formal approach (2018) Swarm Robotics: A Formal Approach, pp. 1-210. Cited 181 times. https://www.springer.com/in/book/9783319745268 ISBN: 978-331974528-2; 978-331974526-8 doi: 10.1007/9783319745282Bayindir, L. A review of swarm robotics tasks (2016) Neurocomputing, 172, pp. 292-321. Cited 265 times. www.elsevier.com/locate/neucom doi: 10.1016/j.neucom.2015.05.116Şahin, E. Swarm robotics: From sources of inspiration to domains of application (2005) Lecture Notes in Computer Science, 3342, pp. 10-20. Cited 549 times. http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40356-69-1180994-0,00.html doi: 10.1007/978-3-540-30552-1_2Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M. Swarm robotics: A review from the swarm engineering perspective (2013) Swarm Intelligence, 7 (1), pp. 1-41. Cited 1064 times. http://www.springer.com doi: 10.1007/s11721-012-0075-2Duarte, 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 (2016) PLoS ONE, 11 (3), art. no. e0151834. Cited 77 times. http://www.plosone.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pone.0151834&representation=PDF doi: 10.1371/journal.pone.0151834Kernbach, S., Thenius, R., Kernbach, O., Schmickl, T. Re-embodiment of honeybee aggregation behavior in an artificial micro-robotic system (2009) Adaptive Behavior, 17 (3), pp. 237-259. Cited 141 times. doi: 10.1177/1059712309104966Kube, C.R., Hong, Z. Collective Robotics: From Social Insects to Robots (Open Access) (1993) Adaptive Behavior, 2 (2), pp. 189-218. Cited 219 times. doi: 10.1177/105971239300200204Holland, Owen, Melhuish, Chris Interactive method for controlling group size in multiple mobile robot systems (Open Access) (1997) International Conference on Advanced Robotics, Proceedings, ICAR, pp. 201-206. Cited 15 times.Mermoud, G., Matthey, L., Evans, W.C., Martinoli, A. Aggregation-mediated collective perception and action in a group of miniature Robots (2010) Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2, pp. 599-606. Cited 17 times. ISBN: 978-161738771-5Soysal, O., Şahin, E. Probabilistic aggregation strategies in swarm robotic systems (2005) Proceedings - 2005 IEEE Swarm Intelligence Symposium, SIS 2005, 2005, art. no. 1501639, pp. 325-332. Cited 111 times. ISBN: 0780389166; 978-078038916-8 doi: 10.1109/SIS.2005.1501639Arvin, F., Samsudin, K., Ramli, A.R., Bekravi, M. Imitation of honeybee aggregation with collective behavior of swarm robots (Open Access) (2011) International Journal of Computational Intelligence Systems, 4 (4), pp. 739-748. Cited 35 times. doi: 10.1080/18756891.2011.9727825Wang, S., Turgut, A.E., Schmickl, T., Lennox, B., Arvin, F. Investigation of Cue-Based Aggregation Behaviour in Complex Environments (2021) Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 350, pp. 18-36. Cited 2 times. http://www.springer.com/series/8197 ISBN: 978-303067539-4 doi: 10.1007/978-3-030-67540-0_2Arvin, F., Doraisamy, S.C., Samsudin, K., Ahmad, F.A., Ramli, A.R. Implementation of a cue-based aggregation with a swarm robotic system (2012) Communications in Computer and Information Science, 295 CCIS, pp. 113-122. Cited 6 times. ISBN: 978-364232825-1 doi: 10.1007/978-3-642-32826-8_12Arvin, 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 (2014) Adaptive Behavior, 22 (3), pp. 189-206. Cited 52 times. doi: 10.1177/1059712314528009Vardy, A. Aggregation in robot swarms using odometry (2016) Artificial Life and Robotics, 21 (4), pp. 443-450. Cited 6 times. http://www.springerlink.com/content/1433-5298 doi: 10.1007/s10015-016-0333-2Arvin, 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 (2018) IEEE International Conference on Intelligent Robots and Systems, art. no. 8593961, pp. 4288-4294. Cited 32 times. ISBN: 978-153868094-0 doi: 10.1109/IROS.2018.8593961Amjadi, A.S., Raoufi, M., Turgut, A.E. (2021) A Self-Adaptive Landmarkbased Aggregation Method for Robot Swarms Adaptive Behavior, 1 JanuaryHereford, J. Analysis of BEECLUST swarm algorithm (2011) IEEE SSCI 2011 - Symposium Series on Computational Intelligence - SIS 2011: 2011 IEEE Symposium on Swarm Intelligence, art. no. 5952587, pp. 192-198. Cited 18 times. ISBN: 978-161284052-9 doi: 10.1109/SIS.2011.5952587Hereford, J. BEECLUST swarm algorithm: Analysis and implementation using a Markov chain model (2013) International Journal of Innovative Computing and Applications, 5 (2), pp. 115-124. Cited 6 times. http://www.inderscience.com/ijica doi: 10.1504/IJICA.2013.053185Hamann, H., Wörn, H., Crailsheim, K., Schmickl, T. Spatial macroscopic models of a bio-inspired robotic swarm algorithm (2008) 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, art. no. 4651038, pp. 1415-1420. Cited 24 times. ISBN: 978-142442058-2 doi: 10.1109/IROS.2008.4651038Schmickl, T., Hamann, H., Wörn, H., Crailsheim, K. Two different approaches to a macroscopic model of a bio-inspired robotic swarm (Open Access) (2009) Robotics and Autonomous Systems, 57 (9), pp. 913-921. Cited 42 times. doi: 10.1016/j.robot.2009.06.002Schmickl, 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 (2009) Autonomous Agents and Multi-Agent Systems, 18 (1), pp. 133-155. Cited 126 times. doi: 10.1007/s10458-008-9058-5Vogrin, M., Stefanec, M., Schmickl, T. Social Distancing in Robot Swarms: Modulating Exploitation and Exploration without Signal Exchange (Open Access) (2020) 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, art. no. 9308502, pp. 2233-2240. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9308061 ISBN: 978-172812547-3 doi: 10.1109/SSCI47803.2020.9308502Bodi, M., Thenius, R., Szopek, M., Schmickl, T., Crailsheim, K. Interaction of robot swarms using the honeybee-inspired control algorithm BEECLUST (Open Access) (2012) Mathematical and Computer Modelling of Dynamical Systems, 18 (1), pp. 87-100. Cited 37 times. doi: 10.1080/13873954.2011.601420Arvin, F., Turgut, A.E., Krajník, T., Yue, S. 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