Birattari, M.

Dorigo, Marco

Ferrante, Eliseo

Huepe, C.

Turgut, A. E.

Wenseleers, T.

English

VU Research Portal

VU Research PortalExplicit and Implicit Directional Information Transfer in Collective MotionFerrante, Eliseo; Turgut, A. E.; Huepe, C.; Birattari, M.; Dorigo, Marco; Wenseleers, T.published inArtificial Life 132012Link to publication in VU Research Portalcitation for published version (APA)Ferrante, E., Turgut, A. E., Huepe, C., Birattari, M., Dorigo, M., & Wenseleers, T. (2012). Explicit and ImplicitDirectional Information Transfer in Collective Motion. In Artificial Life 13 (pp. 551-552)https://www.researchgate.net/publication/236657541_Explicit_and_Implicit_Directional_Information_Transfer_in_Collective_MotionGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.E-mail address:vuresearchportal.ub@vu.nlDownload date: 22. May. 2021© 2012 Massachusetts Institute of Technology Artificial Life 13: 551–577[Explicit and Implicit Directional Information Transfer in Collective MotionE. Ferrante1,2, A. E. Turgut1,2, C. Huepe3, M. Birattari1, M. Dorigo1 and T. Wenseleers21IRIDIA, CoDE, Université Libre de Bruxelles, 50 Av. Franklin Roosevelt CP 194/6, 1050 Brussels, Belgium2Laboratory for Entomology, Katholieke Universiteit Leuven, 59 Naamsestraat - bus 2466, 3000 Leuven, Belgium3Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208, USA]Extended AbstractWe study the cohesive coordinated collective motion of a group of mobile autonomous robots. We use virtual interactionsbetween robots implemented via proximal control, which allows the robots to reach a stable formation using virtual potentialfunctions (Turgut et al., 2008; Ferrante et al., 2011). The alignment component can be seen as a mechanism for directionalinformation transfer (Sumpter et al., 2008). We refer here to information transfer in collective motion as the process throughwhich robot orientation is transferred to its neighbors over time.We consider here two information transfer mechanisms for collective motion in a group of mobile robots. The first oneexploits information transfer through direct communication and requires robots equipped with proximity, orientation sensingand communication devices. We propose communication strategies that allow the robots informed about a desired directionof motion to influence the rest of the group (Couzin et al., 2005; Ferrante et al., 2011). The second mechanism consistsof information transfer without the alignment component and communication (Ferrante et al., 2012), which can be used onsimpler robots only equipped with proximity sensors. We developed a simple motion control mechanism that allows a groupof robots to perform collective motion in a random direction without needing robots informed about a desired direction or anexplicit alignment behavior: information among the robots is thus transferred indirectly.Information transfer via communication We consider a case where some robots have a persistent desired direction ofmotion (desired direction A) which could, for example, represent the direction to a food source. There is also a second desireddirection (desired direction B), only present during a time window which could, for example, represent the escape directionfrom a predator. Desired direction B is in conflict with A: it points in the opposite direction and has higher priority. Theobjective is to move the group in the direction that, at a given time, has the maximum priority, and to keep the group cohesive.We proposed a self-adaptive communication strategy (SCS), that is an extension of two previously proposed strategies (Fer-rante et al., 2011). In SCS, the robot sends an angle ✓s0 and receives angles ✓si from its k neighbors. It computes the averageof the received angles: h =Pki=0ej✓sikPki=0ej✓si k. The angle sent is: ✓s0= 6 [wg + (1− w)h] . The parameter w 2 [0, 1] is the degreeof confidence of the robot on the desired direction g. Non-informed robots use w = 0 (they possess no information about g).Robots informed about desired direction B use w = 1, which makes them stubborn. Robots informed about desired direction Aincrease w when they measure high level of consensus in the information received by the neighbors, and decrease it otherwise.Figure 1a shows the distribution of the accuracy over time, which measures how close the group direction is to desireddirection A. In these experiments, 1% of the robots is always informed about desired direction A. During the time windowwhere an additional 1% of the robots is informed about desired direction B, the accuracy reaching 0 indicates that desireddirection B is being followed. In the remaining part of the experiment, the group correctly follows desired direction A. Thisresult has been validated on real robot experiments (Fig. 1b). In addition, we show that SCS results either in a better accuracy(Fig. 1a and Fig. 1b) or in a better group cohesion (Fig. 1c) than two previously proposed strategies, HCS and ICS. The fullresults are reported in Ferrante et al. (2011).Information transfer without communication We study information transfer with no alignment behavior and no communi-cation. Our approach is based on a novel Magnitude Dependent Motion Control (MDMC) method, used to compute the forwardand angular speed of the robot. The two speeds depend on the magnitude and angle of f , the vector resulting from proximalcontrol that encodes the attraction and repulsion strenght from the neighbors. fxand fydenote the projection of f on the axisparallel (x) and perpendicular (y) to the direction of motion of the robot. In MDMC, the forward speed u is proportional to thex component: u = K1fx + U , and the angular speed ! to the y component: ! = K2fy , where U is a forward biasing speed.Figure 1 (second row) shows the results of experiments performed with simulated and real robots. MDMC is compared to themethod used in Turgut et al. (2008): Magnitude Independent Motion Control (MIMC). In MIMC, the forward and angular speeddo not depend on the magnitude of the vector f but just on its angle. Figure 1d shows the distribution of the order metric overtime, which measures the degree of alignment in the group. MDMC achieves ordered motion without the alignment behavior0 500 1000 15000.00.20.40.60.81.0time (s)Accuracy300  robots ,  3  informedHCSICSSCS(a)0 50 100 150 2000.00.20.40.60.81.0time (s)Accuracy8  robots ,  1  informedHCSICSSCS(b)HCS ICS SCS0123456300  robots ,  3  informedCommunication strategyNumber of groups(c)0 500 1000 1500 2000 250000.20.40.60.81TimeOrder1000 robots − No Alignment  MIMCMDMC(d)0 50 100 150 200 250 30000.20.40.60.818 robots − No AlignmentTimeOrder(e)−505MIMC 0.01MDMC 0.01MIMC 0.05MDMC 0.05MIMC 0.1MDMC 0.1MIMC 0.15MDMC 0.15MIMC 0.2MDMC 0.2100 robots − No AlignmentTraveled distance (m)(f)Figure 1: Experiments with simulated and real robots. Time dependent data is sampled every second. Black lines are themedians of the distribution, whereas grey lines (in (a), (b)) and error bars (in (d), (e)) represent the 25% and the 75% quartiles.and without informed robots, whereas MIMC does requires informed robots or the alignment behavior. These conclusions arebacked up by real robot experiments (Fig. 1e). Moreover, when a proportion of informed robots (0.01, 0.05, 0.1, 0.15, 0.2 asindicated in the plot) is introduced, the group is able to travel further along a desired direction of motion using MDMC thanusing the earlier MIMC method (Fig. 1f).Discussion and conclusion We showed that the information needed to achieve collective motion can be transferred eitherdirectly or indirectly. Direct information transfer requires robots with orientation sensing and communication devices. Wedeveloped a communication strategy that can cope with two conflicting desired directions of motion. We also proposed anovel mechanism for robot motion that exploits indirect information transfer. This allows robots that lack the above mentionedcapabilities to perform cohesive collective motion without communication, showing that implicit information transfer on theheading direction takes place even without communication. In future work, we will use information-theoretic metrics to measureinformation transfer more rigorously.Acknowledgements This work was partially supported by: the European Union (ERC Advanced Grant “E-SWARM”, contract 246939);the F.R.S.-FNRS of Belgium’s French Community (Meta-X project); the Vlaanderen Research Foundation Flanders (H2Swarm project), theUS National Science Foundation (Grant No. PHY-0848755).ReferencesCouzin, I. D., Krause, J., Franks, N. R., and Levin, S. A. (2005). Effective leadership and decision-making in animal groups on the move.Nature, 433:513–516.Ferrante, E., Turgut, A. E., Huepe, C., Stranieri, A., Pinciroli, C., and Dorigo, M. (2012). Self-organized flocking with a mobile robot swarm:a novel motion control method. IridiaTr2012-003, Université Libre de Bruxelles, Belgium.Ferrante, E., Turgut, A. E., Stranieri, A., Pinciroli, C., Birattari, M., and Dorigo, M. (2011). A self-adaptive communication strategy forflocking in stationary and non-stationary environments. IridiaTr2012-002, Université Libre de Bruxelles, Belgium.Sumpter, D. J. T., Buhl, J., Biro, D., and Couzin, I. D. (2008). Information transfer in moving animal groups. Theory in Biosciences,127(2):177–186.Turgut, A. E., Çelikkanat, H., Gökçe, F., and Şahin, E. (2008). Self-organized flocking in mobile robot swarms. Swarm Intelligence,2(2):97–120.Collective Dynamics Extended Abstracts552 Artificial Life 13

Explicit and Implicit Directional Information Transfer in Collective Motion

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Explicit and Implicit Directional Information Transfer in Collective Motion

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