Abstract
Bio-inspired aggregation is one of the most fundamental behaviours that has been studied in swarm robotic for more than two decades. Biology revealed that the environmental characteristics are very important factors in aggregation of social insects and other animals. In this paper, we study the effects of different environmental factors such as size and texture of aggregation cues using real robots. In addition, we propose a mathematical model to predict the behaviour of the aggregation during an experiment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arvin, F., Bekravi, M.: Encoderless Position Estimation and Error Correction Techniques for Miniature Mobile Robots. Turkish Journal of Electrical Engineering & Computer Sciences 21, 1631–1645 (2013)
Arvin, F., Murray, J., Zhang, C., Yue, S.: Colias: An Autonomous Micro Robot for Swarm Robotic Applications. International Journal of Advanced Robotic Systems 11(113), 1–10 (2014)
Arvin, F., Samsudin, K., Ramli, A.R.: Development of IR-Based Short-Range Communication Techniques for Swarm Robot Applications. Advances in Electrical and Computer Engineering 10(4), 61–68 (2010)
Arvin, F., Samsudin, K., Ramli, A.R., Bekravi, M.: Imitation of Honeybee Aggregation with Collective Behavior of Swarm Robots. International Journal of Computational Intelligence Systems 4(4), 739–748 (2011)
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. Adaptive Behavior 22, 189–206 (2014)
Arvin, F., Turgut, A.E., Bellotto, N., Yue, S.: Comparison of different cue-based swarm aggregation strategies. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds.) ICSI 2014, Part I. LNCS, vol. 8794, pp. 1–8. Springer, Heidelberg (2014)
Bayindir, L., Şahin, E.: Modeling self-organized aggregation in swarm robotic systems. In: Swarm Intelligence Symposium, pp. 88–95 (2009)
Camazine, S., Franks, N., Sneyd, J., Bonabeau, E., Deneubourg, J.L., Theraulaz, G.: Self-organization in Biological Systems. Princeton University Press (2001)
Clauset, A., Shalizi, C.R., Newman, M.E.: Power-law distributions in empirical data. SIAM Review 51(4), 661–703 (2009)
Correll, N., Martinoli, A.: Modeling self-organized aggregation in a swarm of miniature robots. In: IEEE International Conference on Robotics and Automation, Workshop on Collective Behaviors Inspired by Biological and Biochemical Systems (2007)
Hu, C., Arvin, F., Yue, S.: Development of a bio-inspired vision system for mobile micro-robots. In: 4th International Conference on Development and Learning and on Epigenetic Robotics, pp. 137–142 (2014)
Krajník, T., Nitsche, M., Faigl, J., Vaněk, P., Saska, M., Přeučil, L., Duckett, T., Mejail, M.: A Practical Multirobot Localization System. Journal of Intelligent & Robotic Systems 76(3–4), 539–562 (2014)
Lerman, K., Galstyan, A., Martinoli, A., Ijspeert, A.: A macroscopic analytical model of collaboration in distributed robotic systems. Artificial Life 7(4), 375–393 (2001)
Martinoli, A., Ijspeert, A., Mondada, F.: Understanding collective aggregation mechanisms: From probabilistic modelling to experiments with real robots. Robotics and Autonomous Systems 29(1), 51–63 (1999)
Mermoud, G., Matthey, L., Evans, W., Martinoli, A.: Aggregation-mediated collective perception and action in a group of miniature robots. In: International Conference on Autonomous Agents and Multiagent Systems, pp. 599–606 (2010)
Şahin, E., Girgin, S., Bayındır, L., Turgut, A.E.: Swarm robotics. In: Swarm Intelligence, vol. 1, pp. 87–100 (2008)
Schmickl, T., Hamann, H., Worn, H., Crailsheim, K.: Two different approaches to a macroscopic model of a bio-inspired robotic swarm. Robotics and Autonomous Systems 57(9), 913–921 (2009)
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. Autonomous Agents and Multi-Agent Systems 18(1), 133–155 (2009)
Schmickl, T., Hamann, H.: BEECLUST: A Swarm Algorithm Derived from Honeybees. Bio-inspired Computing and Communication Networks. CRC Press (2011)
Soysal, O., Şahin, E.: Probabilistic aggregation strategies in swarm robotic systems. In: Swarm Intelligence Symposium, pp. 325–332 (2005)
Soysal, O., Şahin, E.: A macroscopic model for self-organized aggregation in swarm robotic systems. In: Şahin, E., Spears, W.M., Winfield, A.F.T. (eds.) Swarm Robotics Ws. LNCS, vol. 4433, pp. 27–42. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Arvin, F., Attar, A., Turgut, A.E., Yue, S. (2015). Power-Law Distribution of Long-Term Experimental Data in Swarm Robotics. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9140. Springer, Cham. https://doi.org/10.1007/978-3-319-20466-6_58
Download citation
DOI: https://doi.org/10.1007/978-3-319-20466-6_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20465-9
Online ISBN: 978-3-319-20466-6
eBook Packages: Computer ScienceComputer Science (R0)