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A self-adaptive communication strategy for flocking in stationary and non-stationary environments

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Abstract

We propose a self-adaptive communication strategy for controlling the heading direction of a swarm of mobile robots during flocking. We consider the problem where a small group of informed robots has to guide a large swarm along a desired direction. We consider three versions of this problem: one where the desired direction is fixed; one where the desired direction changes over time; one where a second group of informed robots has information about a second desired direction that conflicts with the first one, but has higher priority. The goal of the swarm is to follow, at all times, the desired direction that has the highest priority and, at the same time, to keep cohesion. The proposed strategy allows the informed robots to guide the swarm when only one desired direction is present. Additionally, a self-adaptation mechanism allows the robots to indirectly sense the second desired direction, and makes the swarm follow it. In experiments with both simulated and real robots, we evaluate how well the swarm tracks the desired direction and how well it maintains cohesion. We show that, using self-adaptive communication, the swarm is able to follow the desired direction with the highest priority at all times without splitting.

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Notes

  1. The body-fixed reference frame is right-handed and fixed to the center of a robot: its x-axis points to the front of the robot and its y-axis is coincident with the rotation axis of the wheels.

  2. Swarmanoid project, http://www.swarmanoid.org/ (February 2013).

  3. Carlo Pinciroli, The ARGoS Website, http://iridia.ulb.ac.be/argos/ (February 2013).

  4. Chipmunk-physics - Fast and lightweight 2D rigid body physics library in C - Google Project Hosting, http://code.google.com/p/chipmunk-physics/ (February 2013).

  5. Note that such hats are used for tracking purposes only and are not detectable by the robot themselves.

  6. http://www.halcon.de/.

References

  • Antonelli G, Arrichiello F, Chiaverini S (2010) Flocking for multi-robot systems via the null-space-based behavioral control. Swarm Intell 4(1):37–56

    Article  Google Scholar 

  • Aoki I (1980) An analysis of the schooling behavior of fish: internal organization and communication process. Bull Ocean Res Inst Univ Tokyo 12:1–62

    Google Scholar 

  • Aoki I (1982) A simulation study on the schooling mechanism in fish. Bull Japn Soc Sci Fish 48:1081–1088

    Article  Google Scholar 

  • Baldassarre, Nolfi S, Parisi D (2003) Evolving mobile robots able to display collective behaviors. Artif Life 9(3):255–267

    Article  Google Scholar 

  • Ballerini M, Cabibbo N, Candelier R, Cavagna A, Cisbani E, Giardina I, Lecomte V, Parisi AOG, Procaccini A, Viale M, Zdravkovic V (2008) Interaction ruling animal collective behavior depends on topological rather than metric distance: evidence from a field study. Proc Natl Acad Sci India Sect A 105(4):1232–1237

    Article  Google Scholar 

  • Barnes L, Fields M, Valavanis K (2009) Swarm formation control utilizing elliptical surfaces and limiting functions. IEEE Trans Syst Man Cybern B Cybern Part B 39(6):1434–1445

    Article  Google Scholar 

  • 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 collective-robotic research. In: 2010 IEEE/RSJ international conference on intelligent robots and systems (IROS 2010), IEEE Press, Piscataway, pp 4187–4193

  • Brambilla M, Ferrante E, Birattari M, Dorigo M (2013) Swarm robotics: a review from the swarm engineering perspective. Swarm Intell 7(1):1–41

    Article  Google Scholar 

  • Campo A, Nouyan S, Birattari M, Groß R, Dorigo M (2006) Negotiation of goal direction for cooperative transport. In: Dorigo M, Gambardella LM, Birattari M, Martinoli A, Poli R, Stützle T (eds) Ant colony optimization and swarm intelligence (ANTS) 2006. Lecture notes in computer science, vol 4150. Springer, Berlin, pp 191–202

    Google Scholar 

  • Çelikkanat H, Şahin E (2010) Steering self-organized robot flocks through externally guided individuals. Neural Comput Appl 19(6):849–865

    Article  Google Scholar 

  • Couzin I, Krause J, James R, Ruxton G, Franks N (2002) Collective memory and spatial sorting in animal groups. J Theor Biol 218(1):1–11

    Article  MathSciNet  Google Scholar 

  • Couzin ID, Krause J, Franks NR, Levin SA (2005) Effective leadership and decision-making in animal groups on the move. Nature 433:513–516

    Article  Google Scholar 

  • Diwold K, Schaerf T, Myerscough M, Middendorf M, Beekman M (2011) Deciding on the wing: in-flight decision making and search space sampling in the red dwarf honeybee Apis florea. Swarm Intell 5(2):121–141

    Article  Google Scholar 

  • Dorigo M, Floreano D, Gambardella LM, Mondada F, Nolfi S, Baaboura T, Birattari M, Bonani M, Brambilla M, Brutschy A, Burnier D, Campo A, Christensen A, Decugnière A, Di Caro G, Ducatelle F, Ferrante E, Förster A, Guzzi J, Longchamp V, Magnenat S, Martinez Gonzales J, Mathews N, Montes de Oca M, O’Grady R, Pinciroli C, Pini G, Rétornaz P, Roberts J, Sperati V, Stirling T, Stranieri A, Stützle T, Trianni V, Tuci E, Turgut AE, Vaussard F (2013) Swarmanoid a novel concept for the study of heterogeneous robotic swarms. IEEE Robot Autom Mag 20(4)

  • Ducatelle F, Di Caro G, Pinciroli C, Gambardella LM (2011) Self-organized cooperation between robotic swarms. Swarm Intell 5(2):73–96

    Google Scholar 

  • Ferrante E, Sun W, Turgut AE, Dorigo M, Birattari M, Wenseleers T (2012a) Self-organized flocking with conflicting goal directions. In: Blondel V, Carletti T, Carlon E, Wit AD, Gaspard P, Goldbeter A, Lambiotte R, Vanderzande C (eds) Proceedings of the 12th European conference on complex systems (ECCS 2012). Lecture notes in computer science. Springer, Berlin

  • Ferrante E, Turgut AE, Huepe C, Stranieri A, Pinciroli C, Dorigo M (2012b) Self-organized flocking with a mobile robot swarm: a novel motion control method. Adapt Behav 20(6):460–477

    Article  Google Scholar 

  • Ferrante E, Turgut AE, Mathews N, Birattari M, Dorigo M (2010) Flocking in stationary and non-stationary environments: A novel communication strategy for heading alignment. In: Schaefer R, Cotta C, Kołodziej J, Rudolph G (eds) Parallel Problem Solving from Nature—PPSN XI. Lecture notes in computer science, vol 6239. Springer, Berlin, pp 331–340

    Google Scholar 

  • 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: complete data. Supplementary information http://iridia.ulb.ac.be/supp/IridiaSupp2011-025/. Accessed 6 Aug 2013

  • François G, Sophie B, Nicolas B, Gutierrez M (2006) Waves of agitation inside anchovy schools observed with multibeam sensor: a way to transmit information in response to predation. ICES J Mar Sci 63:1405–1417

    Article  Google Scholar 

  • Franks NR, Hooper JW, Gumn M, Bridger TH, Marshall JAR, Gross R,Dornhaus A (2007) Moving targets: collective decisions and flexible choices in house-hunting ants. Swarm Intell 10(2):81–94

    Google Scholar 

  • Gautrais J, Ginelli F, Fournier R, Blanco S, Soria M, Chaté H, Theraulaz G (2012) Deciphering interactions in moving animal groups. PLoS Comput Biol 8(9):e1002678+

    Google Scholar 

  • Gökçe F, Şahin E (2010) The pros and cons of flocking in the long-range “migration” of mobile robot swarms. Theor Comput Sci 411(21):2140–2154

    Article  MATH  Google Scholar 

  • Hauert S, Winkler L, Zufferey JC, Floreano D (2008) Ant-based swarming with positionless micro air vehicles for communication relay. Swarm Intell 20(2–4):167–188

    Article  Google Scholar 

  • Hayes A, Dormiani-Tabatabaei P (2002) Self-organized flocking with agent failure: off-line optimization and demonstration with real robots. In: Proceedings of the IEEE international conference on robotics and automation (ICRA), IEEE Press, Piscataway, pp 3900–3905

  • Hettiarachchi S, Spears W (2009) Distributed adaptive swarm for obstacle avoidance. Int J Intell Comput Cybern 2(4):644–671

    Article  MATH  MathSciNet  Google Scholar 

  • Holland O, Woods J, Nardi R, Clark A (2005) Beyond swarm intelligence: the ultraswarm. In: Proceedings of the IEEE swarm Intelligence symposium, IEEE Press, Piscataway, pp 217– 224

  • Katz Y, Tunstrøm K, Ioannou CC, Huepe C, Couzin ID (2011) Inferring the structure and dynamics of interactions in schooling fish. Proc Natl Acad Sci India Sect A 108(46):18720–18725

    Article  Google Scholar 

  • Kelly I, Keating D (1996) Flocking by the fusion of sonar and active infrared sensors on physical autonomous robots. In: Proceedings of the third international conference on mechatronics and machine vision in practice (M2VIP), pp 14–17

  • Makinson JC, Oldroyd BP, Schaerf TM, Wattanachaiyingcharoen W, Beekman M (2011) Moving home: nest-site selection in the red dwarf honeybee (Apis florea). Behav Ecol Sociobiol 65(5):945–958

    Article  Google Scholar 

  • Matarić MJ (1994) Interaction and intelligent behavior. PhD thesis, MIT, Boston, MA

  • Monteiro S, Bicho E (2010) Attractor dynamics approach to formation control: theory and application. Auton Robots 29(3–4):331–355

    Article  Google Scholar 

  • Montes de Oca MA, Ferrante E, Scheidler A, Pinciroli C, Birattari M, Dorigo M (2011) Majority-rule opinion dynamics with differential latency: a mechanism for self-organized collective decision-making. Swarm Intell 5(3–4):305–327

    Article  Google Scholar 

  • Moshtagh N, Jadbabaie A, Daniilidis K (2006) Vision-based control laws for distributed flocking of nonholonomic agents. In: Proceedings of the IEEE international conference on robotics and automation, Orlando, FL, pp 2769–2774

  • Moslinger C, Schmickl T, Crailsheim K (2009) A minimalistic flocking algorithm for swarm robots. In: Kampis G, Karsai I, Szathmà àry E (eds) European conference of artificial life (ECAL), Springer, Berlin

  • Nembrini J, Winfield AFT, Melhuish C (2002) Minimalist coherent swarming of wireless networked autonomous mobile robots. In: Hallam B, Floreano D (eds) From animals to animats, vol 7. MIT Press,Cambridge, MA, pp 273–382

    Google Scholar 

  • 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):271–295

    Article  Google Scholar 

  • Pini G, Brutschy A, Frison M, Roli A, Dorigo M, Birattari M (2011) Task partitioning in swarms of robots: an adaptive method for strategy selection. Swarm Intell 5(3–4):283–304

    Article  Google Scholar 

  • Press W, Teukolsky S, Vetterling W, Flannery B (1992) Numerical recipes in C, 2nd edn. Cambridge University Press, Cambridge

    Google Scholar 

  • Regmi A, Sandoval R, Byrne R, Tanner H, Abdallah C (2005) Experimental implementation of flocking algorithms in wheeled mobile robots. In: Jayasuriya S (ed) Proceedings of the American control conference, vol 7. IEEE Press, Piscataway, pp 4917– 4922

  • Reynolds C (1987) Flocks, herds and schools: a distributed behavioral model. In: Stone MC (ed) SIGGRAPH ’87: Proceedings of the 14th annual conference on computer graphics and interactive techniques, ACM Press, New York, pp 25–34

  • Roberts J, Stirling T, Zufferey J, Floreano D (2009) 2.5d infrared range and bearing system for collective robotics. In: Papanikolopoulos N, Sugano S, Chiaverini S, Meng M (eds) IEEE/RSJ international conference on intelligent robots and systems, IEEE Press, Piscataway

  • Simpson SJ, Sword GA, Lorch PD, Couzin ID (2006) Cannibal crickets on a forced march for protein and salt. Proc Natl Acad Sci USA 103(11):4152-4156

    Article  Google Scholar 

  • Spears WM, Spears DF, Hamann JC, Heil R (2004) Distributed, physiscs-based control of swarms of vehicles. Auton Robots 17:137–162

    Article  Google Scholar 

  • Sperati V, Trianni V, Nolfi S (2011) Self-organised path formation in a swarm of robots. Swarm Intell 5(2):97–119

    Article  Google Scholar 

  • Stranieri A, Ferrante E, Turgut A, Trianni V, Pinciroli C, Birattari M, Dorigo M (2011) Self-organized flocking with a heterogeneous mobile robot swarm. In: Lenaerts T, Giacobini M, Bersini H, Borgine P, Dorigo M, Doursat R (eds) Proceedings of ECAL 2001, MIT Press, Cambridge, MA, pp 789–796

  • Turgut AE, Çelikkanat H, Gökçe F, Şahin E (2008) Self-organized flocking in mobile robot swarms. Swarm Intell 2(2):97–120

    Google Scholar 

  • Vicsek T, Czirok A, Ben-Jacob E, Cohen I, Shochet O (1995) Novel type of phase transition in a system of self-driven particles. Phys Rev Lett 75(6):1226–1229

    Google Scholar 

  • Yu C, Werfel J, Nagpal R (2010) Collective decision-making in multi-agent systems by implicit leadership. In: van der Hoek, Kaminka, Lesprance, Luck, Sen (eds) Proceedings of 9th internatial conference on autonomous agents and multiagent systems (AAMAS2010), International Foundation for Autonomous Agents and Multiagent Systems, Toronto, Canada

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Acknowledgments

This work was partially supported by the European Union through the ERC Advanced Grant “E-SWARM: Engineering Swarm Intelligence Systems” (contract 246939) and the Future and Emerging Technologies project ASCENS and by the Vlaanderen Research Foundation Flanders (Flemish Community of Belgium) through the H2Swarm project. The information provided is the sole responsibility of the authors and does not reflect the European Commission’s opinion. The European Commission is not responsible for any use that might be made of data appearing in this publication. Mauro Birattari, and Marco Dorigo acknowledge support from the F.R.S.-FNRS of Belgium’s French Community, of which they are a Research Associate and a Research Director, respectively.

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Authors and Affiliations

  1. IRIDIA, CoDE, Université Libre de Bruxelles, 50 Av. Franklin Roosevelt CP 194/6, 1050, Brussels, Belgium

    Eliseo Ferrante, Alessandro Stranieri, Carlo Pinciroli, Mauro Birattari & Marco Dorigo

  2. Laboratory of Socioecology and Social Evolution, Katholieke Universiteit Leuven, 59 Naamsestraat—Bus 2466, 3000, Louvain, Belgium

    Eliseo Ferrante

  3. Mechatronics Department, THK University, Turkkusu Campus, 06790, Etimesgut, Ankara, Turkey

    Ali Emre Turgut

  4. Department of Computer Science, University of Paderborn, Zukunftsmeile 1, 33102, Paderborn, Germany

    Marco Dorigo

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  1. Eliseo Ferrante
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  2. Ali Emre Turgut
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  4. Carlo Pinciroli
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Correspondence to Eliseo Ferrante.

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Ferrante, E., Turgut, A.E., Stranieri, A. et al. A self-adaptive communication strategy for flocking in stationary and non-stationary environments. Nat Comput 13, 225–245 (2014). https://doi.org/10.1007/s11047-013-9390-9

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