Abstract
In flocking, a large number of individuals move cohesively in a common direction. Many examples can be found in nature: from simple organisms such as crickets and locusts to more complex ones such as birds, fish and quadrupeds. In this paper, we study the flocking behavior of a swarm of robots where information about two distinct goal directions is present in the swarm. In general, we can identify three different macroscopic objectives that we might want to attain: (a) a swarm that moves to the average direction among the two (for example to avoid the obstacle) without splitting; (b) a swarm that selects the most important of the two directions (for example the direction to avoid danger) and follows it without splitting; (c) a swarm that splits in a controlled fashion in the two directions (for example, in the parallel task execution case). This paper proposes a solution for the first objective: a method for moving the swarm along the average between the two conflicting goal directions. We show that this objective can be attained by simply using a similar methodology as the one proposed in earlier work. We execute systematic experiments using a realistic robotics simulator. In the experiments, a small proportion of robots is informed about one goal direction, another small proportion about the other goal direction, and the rest of the swarm is non-informed. We study the effect of what we believe are the critical parameters: the overall proportion of informed robots, the difference between the size of the two groups of informed robots and the difference between the two goal direction. We show that, using the proposed method, the system is always able to follow the average direction between the two.
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References
Reynolds C (1987) Flocks, herds and schools: a distributed behavioral model. In: Stone MC (ed) Proc of the 14th annual conference on computer graphics and interactive techniques, SIGGRAPH’87. ACM Press, New York, pp 25–34
Mataric MJ (1994) Interaction and intelligent behavior
Turgut AE, Çelikkanat H, Gökçe F, Şahin E (2008) Self-organized flocking in mobile robot swarms. Swarm Intell 2(2):97–120
Spears WM, Spears DF, Hamann JC, Heil R (2004) Distributed, physics-based control of swarms of vehicles. Auton Robots 17:137–162
Couzin ID, Krause J, Franks NR, Levin SA (2005) Effective leadership and decision-making in animal groups on the move. Nature 433:513–516
Çelikkanat H, Turgut A, Şahin E (2008) Guiding a robot flock via informed robots. In: Asama H, Kurokawa H, Ota J, Sekiyama K (eds) Distributed autonomous robotic systems (DARS 2008). Springer, Berlin, pp 215–225
Suranga Hettiarachchi WMS (2009) Distributed adaptive swarm for obstacle avoidance. Int J Intell Comput Cybern 2(4):644–671
Ferrante E, Turgut AE, Huepe C, Stranieri A, Pinciroli C, Dorigo M (2012) Self-organized flocking with a mobile robot swarm: a novel motion control method. Adapt Behav. doi:10.1177/1059712312462248
Bonani M, Longchamp V, Magnenat S, Rétornaz P, Burnier D, Roulet G, Vaussard F, Bleuler H, Mondada F (2010) The marxbot, a miniature mobile robot opening new perspectives for the collectiverobotic research. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE Comput Soc, Washington, pp 4187–4193
Pinciroli C, Trianni V, O’Grady R, Pini G, Brutschy A, Brambilla M, Mathews N, Ferrante E, Di Caro G, Ducatelle F, Birattari M, Gambardella LM, Dorigo M (2012) ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell 6(4). doi:10.1007/s11721-012-0072-5
Ferrante E, Turgut AE, Stranieri A, Pinciroli C, Birattari M, Dorigo M (2011) A self-adaptive communication strategy for flocking in stationary and non-stationary environments. IRIDIA Tech-rep IridiaTr2012-002
Acknowledgements
This work was supported by the European Union (ERC Advanced Grant “E-SWARM: Engineering Swarm Intelligence Systems” (contract 246939) and FET project ASCENS) and by the Vlaanderen Research Foundation Flanders (H2Swarm project). Mauro Birattari, and Marco Dorigo acknowledge support from the F.R.S.-FNRS of Belgium’s French Community.
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Ferrante, E., Sun, W., Turgut, A.E., Dorigo, M., Birattari, M., Wenseleers, T. (2013). Self-organized Flocking with Conflicting Goal Directions. In: Gilbert, T., Kirkilionis, M., Nicolis, G. (eds) Proceedings of the European Conference on Complex Systems 2012. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-00395-5_74
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DOI: https://doi.org/10.1007/978-3-319-00395-5_74
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