Abstract
This paper investigates the self-organized task allocation behaviors in swarm systems by means of evolutionary game theory. A group of E-puck robots are employed to study the potential factors influencing the strategy choices of tasks that require different costs. Endowing the cooperation and defection strategy to robots, we find possible approaches to promote cooperation among selfish robots without explicit communication or cooperation mechanisms. Irrespective of the global information and centralized control, the proposed method is related with the strategy evolution adopted by the robots performing the tasks. Results are presented for a system of physical robots capable of moving and collectively form a specified spatial pattern. The contribution is that evolutionary game theory offers a new approach to environment-specific task modelling in collective robots.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agassounon W, Martinoli A, Goodman R (2001) A scalable, distributed algorithm for allocating workers in embedded systems. IEEE international conference on systems 5(2):3367–3373
Brutschy A, Pini G, Pinciroli C, Birattari M, Dorigo M (2014) Self-organized task allocation to sequentially interdependent tasks in swarm robotics. Auton Agent Multi-Agent Syst 28(1):101–125
Campo A, Dorigo, M (2007) Efficient multi-foraging in swarm robotics. European conference on advances in artificial life, vol 4648 LNAI. Springer, Berlin, pp 696–705
Duarte A, Weissing FJ, Pen I, Keller L (2011) An evolutionary perspective on self-organized division of labor in social insects. Annu Rev Ecol Evol Syst 42(1):91–110
Gerdes J, Becker MHG, Key G, Cattoretti G, Sanders HW, Smith AG et al (1992) Letters to the editor. J Pathol 168(1):85–87
Karsai I, Wenzel JW (1998) Productivity, individual-level and colony-level flexibility, and organization of work as consequences of colony size. Proc Natl Acad Sci U S A 95(15):8665–8669
Kreiger M, Billeter J (2000) The call of duty: self-organized task allocation in a population of up to twelve mobile robots. Robot Auton Syst 30(1):65–84
Wilson EO (1978) Caste and ecology in the social insects. Monogr Popul Biol 12(3):1
Robinson GE (1992) Regulation of division of labor in insect societies. Annu Rev Entomol 37(1):637
Seeley TD (1989) The honey bee colony as a superorganism. Am Sci 77(6):546–553
Sendova-Franks A, Franks NR (1993) Task allocation in ant colonies within variable environments (a study of temporal polyethism: experimental). Bull Math Biol 55(1):75–96
Stewart AJ, Plotkin JB (2016) Small groups and long memories promote cooperation. Sci Rep 6(s 1–3): 26889
Stivala A, Kashima Y, Kirley M (2016) Culture and cooperation in a spatial public goods game. Phys Rev E 94(3–1):032303
Van den Berg P, Molleman L, Weissing FJ (2015) Focus on the success of others leads to selfish behavior. Proc Nat Acad Sci U S A 112(9):2912–2917
Xu Z, Zhang J, Zhang C, Chen Z (2016) Fixation of strategies driven by switching probabilities in evolutionary games. Europhys Lett 116(5):58002
Zhang J, Chen Z (2016) Contact-based model for strategy updating and evolution of cooperation. Physa D Nonlinear Phenom 323–324(2):27–34
Zhang J, Zhang C, Cao M, Weissing FJ (2015) Crucial role of strategy updating for coexistence of strategies in interaction networks. Phys Rev E Stat Nonlin Soft Matter Phys 91(4):042101
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, Q., Yang, X., Zhu, Y., Zhang, J. (2018). Self-organized Task Allocation in a Swarm of E-puck Robots. In: Deng, Z. (eds) Proceedings of 2017 Chinese Intelligent Automation Conference. CIAC 2017. Lecture Notes in Electrical Engineering, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-10-6445-6_17
Download citation
DOI: https://doi.org/10.1007/978-981-10-6445-6_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6444-9
Online ISBN: 978-981-10-6445-6
eBook Packages: EngineeringEngineering (R0)