Abstract
Swarm robots are used in robotic applications where it is difficult or impossible for a single robot to accomplish a task. In this paper, we study multi-robot, multi-target search problem in an unknown environment. Our goal is to use a group of distributed cooperative mobile robots to find position of an object which is emitting the strongest intensity of radio frequency in the environment. We propose a novel algorithm based on Bee Swarm Optimization (BSO) which is able to automatically find the object. Our experimental results, simulated on a set of random benchmarks, show that the algorithm is able to outperform the state-of-the-art techniques, in particular Particle Swarm Optimization (PSO). We show that our algorithm can be 50.6% more effective for this application in comparison to PSO.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Acar, E., Choset, H., Zhang, Y., Schervish, M.: Path planning for robotic demining: Robust sensor-based coverage of unstructured environments and probabilistic methods. The International Journal of Robotics Research 22, 441–466 (2003)
Gage, D.: Many-robot mcm search systems. In: Proceedings of the Autonomous Vehicles in Mine Countermeasures Symposium, vol. 9, pp. 56–64. Citeseer (1995)
Kantor, G., Singh, S., Peterson, R., Rus, D., Das, A., Kumar, V., Pereira, G., Spletzer, J.: Distributed search and rescue with robot and sensor teams. In: Field and Service Robotics, pp. 529–538 (2006)
Jennings, J., Whelan, G., Evans, W.: Cooperative search and rescue with a team of mobile robots. In: Proceedings of the 8th International Conference on Advanced Robotics, ICAR 1997, pp. 193–200. IEEE (1997)
Marjovi, A., Nunes, J., Marques, L., de Almeida, A.: Multi-robot exploration and fire searching. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 1929–1934. IEEE (2009)
Landis, G.: Robots and humans: Synergy in planetary exploration. In: AIP Conference Proceedings, vol. 654, p. 853 (2003)
Schilling, K., Jungius, C.: Mobile robots for planetary exploration. Control Engineering Practice 4, 513–524 (1996)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)
Doctor, S., Venayagamoorthy, G., Gudise, V.: Optimal pso for collective robotic search applications. In: Congress on Evolutionary Computation, CEC 2004, vol. 2, pp. 1390–1395. IEEE (2004)
Pugh, J., Segapelli, L., Martinoli, A.: Applying aspects of multi-robot search to particle swarm optimization. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 506–507. Springer, Heidelberg (2006)
Pugh, J., Martinoli, A.: Inspiring and modeling multi-robot search with particle swarm optimization. In: IEEE Swarm Intelligence Symposium, SIS 2007, pp. 332–339. IEEE (2007)
Couceiro, M., Rocha, R., Ferreira, N.: A novel multi-robot exploration approach based on particle swarm optimization algorithms. In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 327–332. IEEE (2011)
Hereford, J.: A distributed particle swarm optimization algorithm for swarm robotic applications. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 1678–1685. IEEE (2006)
Derr, K., Manic, M.: Multi-robot, multi-target particle swarm optimization search in noisy wireless environments. In: 2nd Conference on Human System Interactions, HSI 2009, pp. 81–86. IEEE (2009)
Akbari, R., Mohammadi, A., Ziarati, K.: A novel bee swarm optimization algorithm for numerical function optimization. Communications in Nonlinear Science and Numerical Simulation 15, 3142–3155 (2010)
Michel, O.: Webotstm: Professional mobile robot simulation, arXiv preprint cs/0412052 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Najd Ataei, H., Ziarati, K., Eghtesad, M. (2013). A BSO-Based Algorithm for Multi-robot and Multi-target Search. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-38577-3_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38576-6
Online ISBN: 978-3-642-38577-3
eBook Packages: Computer ScienceComputer Science (R0)