Abstract
This paper presents a novel swarm robotics application of chemotaxis behaviour observed in microorganisms. This approach was used to cause exploration robots to return to a work area around the swarm’s nest within a boundless environment. We investigate the performance of our algorithm through extensive simulation studies and hardware validation. Results show that the chemotaxis approach is effective for keeping the swarm close to both stationary and moving nests. Performance comparison of these results with the unrealistic case where a boundary wall was used to keep the swarm within a target search area showed that our chemotaxis approach produced competitive results.
Supported by National Information Technology Development Agency, Nigeria. Simulations were undertaken on ARC3, part of the High Performance Computing facilities at the University of Leeds, UK.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Two robots were used due to availability of robot hardware. Validation with more robots will be done in future work.
References
Arvin, F., et al.: \(\phi \) clust: Pheromone-based aggregation for robotic swarms. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4288–4294. IEEE (2018)
Bayindir, L.: A review of swarm robotics tasks. Neurocomputing 172, 292–321 (2016)
Couceiro, M.S., Figueiredo, C.M., Rocha, R.P., Ferreira, N.M.: Darwinian swarm exploration under communication constraints: initial deployment and fault-tolerance assessment. Robot. Auton. Syst. 62(4), 528–544 (2014)
Hoff, N., Wood, R., Nagpal, R.: Distributed colony-level algorithm switching for robot swarm foraging. In: Martinoli, A., et al. (eds.) Distributed Autonomous Robotic Systems. STAR, vol. 83, pp. 417–430. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-32723-0_30
Lima, D.A., Oliveira, G.M.B.: A probabilistic cellular automata ant memory model for a swarm of foraging robots. In: 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, pp. 1–6. IEEE, November 2017
Lu, Q., Hecker, J.P., Moses, M.E.: Multiple-place swarm foraging with dynamic depots. Auton. Robot. 42(4), 909–926 (2018)
Ngo, T.D., Hung, P.D., Pham, M.T.: A Kangaroo inspired heterogeneous swarm of mobile robots with global network integrity for fast deployment and exploration in large scale structured environments. In: 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), pp. 1205–1212. IEEE, December 2014
Pierce-Shimomura, J.T., Dores, M., Lockery, S.R.: Analysis of the effects of turning bias on chemotaxis in C. elegans. J. Exp. Biol. 208(24), 4727–4733 (2005)
Schmickl, T., Hamann, H.: BEECLUST: a swarm algorithm derived from honeybees. In: Bio-Inspired Computing and Communication Networks, pp. 95–137 (2011)
Tolba, S., Ammar, R.: Virtual Tether Search: a self-constraining search algorithm for swarms in an open ocean. In: 2017 IEEE Symposium on Computers and Communications (ISCC), pp. 1128–1135. IEEE, July 2017
Trianni, V., Campo Alexandre, A.: Fundamental collective behaviors in swarm robotics. In: Kacprzyk, J., Pedrycz, W. (eds.) Springer Handbook of Computational Intelligence, pp. 1377–1394. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-43505-2_71
Ward, S.: Chemotaxis by the nematode Caenorhabditis elegans: identification of attractants and analysis of the response by use of mutants. Proc. Natl. Acad. Sci. U.S.A. 70(3), 817–21 (1973)
Winfield, A.F.T., Liu, W., Nembrini, J., Martinoli, A.: Modelling a wireless connected swarm of mobile robots. Swarm Intell. 2(2–4), 241–266 (2008)
Yu, P., Yan, R., Yao, L.: Measurement of acoustic attenuation coefficient of stored grain. In: 3rd International Conference on Control, Automation and Robotics, pp. 551–554 (2017)
Zedadra, O., Jouandeau, N., Seridi, H., Fortino, G.: Multi-Agent Foraging: state-of-the-art and research challenges. Complex Adapt. Syst. Model. 5(1), 3 (2017)
Zedadra, O., Seridi, H., Jouandeau, N., Fortino, G.: An energy-aware algorithm for large scale foraging systems. Scalable Comput. 16(4), 449–466 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Obute, S.O., Dogar, M.R., Boyle, J.H. (2019). Chemotaxis Based Virtual Fence for Swarm Robots in Unbounded Environments. In: Martinez-Hernandez, U., et al. Biomimetic and Biohybrid Systems. Living Machines 2019. Lecture Notes in Computer Science(), vol 11556. Springer, Cham. https://doi.org/10.1007/978-3-030-24741-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-24741-6_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24740-9
Online ISBN: 978-3-030-24741-6
eBook Packages: Computer ScienceComputer Science (R0)