Abstract
We address the problem of multiple signal source localization where robotic swarms are used to locate multiple signal sources like light, sound, heat, leaks in pressurized systems, hazardous plumes/aerosols resulting from nuclear or chemical spills, fire-origins in forest fires, hazardous chemical discharge in water bodies, oil spills, deep-sea hydrothermal vent plumes, etc. In particular, we present a multi-robot system that implements a modified version of the glowworm swarm optimization (GSO) algorithm, which is originally developed to solve multimodal function optimization problems, for this purpose. The GSO algorithm uses a leapfrogging behavior for the basic search capability and an adaptive decision range that enables the agents to partition into disjoint subgroups, simultaneously taxis towards, and rendezvous at, multiple source locations of interest. Transition of agent behaviors from simulation to real-robot-implementation needs modifications to certain algorithmic aspects mainly because of the point-agent model of the basic GSO algorithm and the physical dimensions and dynamics of a real robot. We briefly describe the basic GSO algorithm and the modifications incorporated into the algorithm in order to make it suitable for a robotic implementation. Realization of each sensing-decision-action cycle of the GSO algorithm requires the robots to perform subtasks such as identification and localization of neighbors, selection of a leader among current neighbors, updating of the associated luciferin and local-decision range, and making a step-movement towards the selected leader. Experiments in this regard validate each robot’s capability to perform the above basic algorithmic primitives. Real-robot-experiments are conducted in the context of light source localization in order to validate the GSO approach to localization of signal sources. These experiments constitute a first step toward implementation in multiple robots for detection of multiple sources.
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
Craenen BGW Eiben AE Computational Intelligence. Encyclopedia of Life Support Sciences, EOLSS Co. Ltd.
Fronczek, J.W., Prasad, N.R.: Bio-inspired sensor swarms to detect leaks in pressurized systems. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics (2005)
Zarzhitsky, D., Spears, D.F., Spears, W.M.: Swarms for chemical plume tracing. In: Proceedings of IEEE Swarm Intelligence Symposium (2005)
Casbeer, D.W., Beard, R.W., McLain, T.W., Li, S.-M., Mehra, R.K.: Forest fire monitoring with multiple small UAVs. In: Proceedings of American Control Conference (2005)
Farrell, J., Li, W., Pang, S., Arrieta, R.: Chemical plume tracing experimental results with a REMUS AUV. In: Proceedings of Oceans 2003 Marine Technology and Ocean Science Conference (2003)
Clark, J., Fierro, R.: Cooperative hybrid control of robotic sensors for perimeter detection and tracking. In: Proceedings of American Control Conference (2005)
Hayes, A.T., Martinoli, A., Goodman, R.M.: Robotica 21, 427–441 (2003)
Dhariwal, A., Sukhatme, G.S., Requicha, A.A.G.: Bacterium-inspired robots for environmental monitoring. In: Proceedings of IEEE International Conference on Robotics and Automation (2004)
Grasso, F.W., Consi, T.R., Mountain, D.C., Atema, J.: Robotics and Autonomous Systems 30, 115–131 (2000)
Lytridis, C., Virk, G.S., Kadar, E.E.: Co-operative smell-based navigation for mobile robots. In: Proceedings of the 7th International Conference CLAWAR 2004. Springer, Heidelberg (2004)
Sandini, G., Lucarini, G., Varoli, M.: Gradient driven self-organizing systems. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (1993)
Farrell, J.A., Pang, S., Li, W.: IEEE Transactions on System, Man, and Cybernetics 33, 850–863 (2003)
Jakuba, M.V.: Stochastic mapping for chemical plume source localization with application to autonomous hydrothermal vent discovery. PhD Thesis, Massachusetts Institute of Technology and Woods Hole Oceanographic Institution (2007)
Cui, X., Hardin, C.T., Ragade, R.K., Elmaghraby, A.S.: A swarm approach for emission sources localization. In: Proceedings of the 16th International Conference on Tools with Artificial Intelligence (2004)
Lilienthal, A., Duckett, T.: Robotics and Autonomous Systems 48, 3–16 (2004)
Krishnanand, K.N., Ghose, D.: Multiagent and Grid Systems: Special Issue on Recent Progress in Distributed Intelligence 3, 209–222 (2006)
Krishnanand, K.N., Ghose, D.: Glowworm swarm optimization algorithm for hazard sensing in ubiquitous environments using heterogeneous agent swarms. In: Prasad, B. (ed.) Soft Computing Applications in Industry. Studies in Fuzziness and Soft Computing, vol. 226, pp. 165–187. Springer, Berlin (2008)
Krishnanand, K.N., Ghose, D.: Chasing multiple mobile signal sources: A glowworm swarm optimization approach. In: 3rd International Conference on Artificial Intelligence (2007)
Krishnanand, K.N., Ghose, D.: Robotics and Autonomous Systems 53, 194–213 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Krishnanand, K.N., Ghose, D. (2009). A Glowworm Swarm Optimization Based Multi-robot System for Signal Source Localization. In: Liu, D., Wang, L., Tan, K.C. (eds) Design and Control of Intelligent Robotic Systems. Studies in Computational Intelligence, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89933-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-89933-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89932-7
Online ISBN: 978-3-540-89933-4
eBook Packages: EngineeringEngineering (R0)