Morphogen diffusion algorithms for tracking and herding using a swarm of kilobots

Focus

Abstract

This paper investigates self-organised collective formation control using swarm robots. In particular, we focus on collective tracking and herding using a large number of very simple robots. To this end, we choose kilobots as our swarm robot test bed due to its low cost and attractive operational scalability. Note, however, that kilobots have extremely limited locomotion, sensing and communication capabilities. To handle these limitations, a number of new control algorithms based on morphogen diffusion and network connectivity preservation have been suggested for collective object tracking and herding. Numerical simulations of large-scale swarm systems as well as preliminary physical experiments with a relatively small number of kilobots have been performed to verify the effectiveness of the proposed algorithms.

Keywords

Swarm robotics Object tracking Morphogen diffusion Network connectivity preservation Kilobots 

References

  1. Abidin ZZ, Arshad MR, Ngah UK (2015) An introduction to swarming robotics: application development trends. Artif Intell Rev 43(4):501–514CrossRefGoogle Scholar
  2. Halme, AJ (2012) Kilobot simulator. https://github.com/ajhalme/kbsim
  3. Jin Y, Guo H, Meng Y (2012) A hierarchical gene regulatory network for adaptive multirobot pattern formation. IEEE Trans Syst Man Cybern Part B Cybern 42(3):805–816CrossRefGoogle Scholar
  4. Kondo S, Miura T (2010) Reaction-diffusion model as a framework for understanding biological pattern formation. Science 329:1616–1620MathSciNetCrossRefMATHGoogle Scholar
  5. Mamei M, Vasirani M, Zambonelli F (2004) Experiments of morphogenesis in swarms of simple mobile robots. Appl Artif Intell 18:903–919CrossRefGoogle Scholar
  6. Mondadal F (2009) The e-puck, a robot designed for education in engineering. In: 9th Conference on autonomous robot systems and competitionsGoogle Scholar
  7. Nagpal R, Shrobe H, Bachrach J (2003) Organizing a global coordinate system from local information on an ad hoc sensor network, vol 2634. IPSN, LNCSSpringer, Berlin HeidelbergGoogle Scholar
  8. Navarro I, Matia F (2009) A proposal of a set of metrics for collective movement of robots. In: Proceedings of workshop on good experimental methodology in robotics, robotics science and systemsGoogle Scholar
  9. Navarro I, Matia F (2013) A survey of collective movement of mobile robots. Int J Adv Robot Syst 10:1–9Google Scholar
  10. Oh H, Jin Y (2014) Evolving hierarchical gene regulatory networks for morphogenetic pattern formation of swarm robots. In: IEEE congress on evolutionary computation (CEC). Beijing, China, JulyGoogle Scholar
  11. Oh H, Shiraz AR, Jin Y (2014) Adaptive swarm robot region coverage using gene regulatory networks. Advances in autonomous robotics systems, lecture notes in computer science 8717:197–208CrossRefGoogle Scholar
  12. Okubo A (1986) Dynamical aspects of animal grouping: swarms, schools, flocks, and herds. Adv Biophys 22:1–94CrossRefGoogle Scholar
  13. Reynolds CW (1987) Flocks, herds, and schools: a distributed behavioral model. Comput Gr 21:25–34CrossRefGoogle Scholar
  14. Rubenstein M, Ahler C, Nagpal. Kilobot R (2012) A low cost scalable robot system for collective behaviors. In: IEEE international conference on robotics and automation (ICRA). pp 3293–3298Google Scholar
  15. Sayama H (2010) Robust morphogenesis of robotic swarms. IEEE Comput Intell Mag 5(3):43–49CrossRefGoogle Scholar
  16. Vartholomeos P, Papadopoulos E (2006) Analysis, design and control of a planar micro-robot driven by two centripetal-force actuators. In: IEEE international conference on robotics and automation (ICRA). Orlando, Florida, MayGoogle Scholar
  17. Winfield AFT, Nembrini J (2012) Emergent swarm morphology control of wireless networked mobile robots, volume 8 of morphogenetic engineering, understanding complex systems. Springer, Berlin HeidelbergGoogle Scholar
  18. Yeom K (2010) Bio-inspired automatic shape formation for swarms of self-reconfigurable modular robots. In: IEEE fifth international conference on bio-inspired computing: theories and applications (BIC-TA). pp 469–476Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Aeronautical and Automotive EngineeringLoughborough UniversityLoughboroughUK
  2. 2.Department of Computer ScienceUniversity of SurreyGuildfordUK

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