Abstract
We investigate the emergence of specialized groups in a swarm of robots, using a simplified version of the stick-pulling problem [5], where the basic task requires the collaboration of two robots in asymmetric roles. We expand our analytical model [4] and identify conditions for optimal performance for a swarm with any number of species. We then implement a distributed adaptation algorithm based on autonomous performance evaluation and parameter adjustment of individual agents. While this algorithm reliably reaches optimal performance, it leads to unbounded parameter distributions. Results are improved by the introduction of a direct parameter exchange mechanism between selected high- and low-performing agents. The emerging parameter distributions are bounded and fluctuate between tight unimodal and bimodal profiles. Both the unbounded optimal and the bounded bimodal distributions represent partitions of the swarm into two specialized groups.
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
Balch, T.: Hierarchic social entropy: An information theoretic measure of robot team diversity. Autonomous Robots 8(3), 209–238 (2000)
Berman, S., Halasz, A., Hsieh, M.A., Kumar, V.: Optimized stochastic policies for task allocation in swarms of robots. IEEE Transactions on Robotics 25(4), 927–937 (2009)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From natural to artificial systems. Oxford University Press, New Yok (1999)
Hsieh, M.A., Halasz, A.M., Cubuk, E.D., Schoenholz, S., Martinoli, A.: Specialization as an optimal strategy under varying external conditions. In: Proceedings of the International Conference on Robotics and Automation (ICRA), Kobe, Japan (2009)
Ijspeert, A., Martinoli, A., Billard, A., Gambardella, L.M.: Collaboration through the Exploitation of Local Interactions in Autonomous Collective Robotics: The Stick Pulling Experiment. Autonomous Robots 11(2), 149–171 (2001), doi:10.1023/A:1011227210047
Kussell, E., Leibler, S.: Phenotypic diversity, population growth, and information in fluctuating environments. Science 309(5743), 2075–2078 (2005)
Lerman, K., Galstyan, A., Martinoli, A., Ijspeert, A.J.: A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems. Artificial Life 7(4), 375–393 (2001), doi:10.1162/106454601317297013
Li, L., Martinoli, A., Abu-Mostafa, Y.: Learning and Measuring Specialization in Collaborative Swarm Systems. Adaptive Behavior 12(3-4), 199–212 (2004); Special issue on Mathematics and Algorithms of Social Interactions, Anderson, C., Balch, T. (eds.), doi:10.1177/105971230401200306
Matari’c, M.J.: Designing and understanding adaptive group behavior. Adaptive Behavior 4, 50–81 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Halász, Á.M., Liang, Y., Hsieh, M.A., Lai, HJ. (2013). Emergence of Specialization in a Swarm of Robots. In: Martinoli, A., et al. Distributed Autonomous Robotic Systems. Springer Tracts in Advanced Robotics, vol 83. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32723-0_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-32723-0_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32722-3
Online ISBN: 978-3-642-32723-0
eBook Packages: EngineeringEngineering (R0)