Abstract
This study proposes an inverted ant cellular automata (IACA) model for swarm robots performing the surveillance task. A new distributed coordination strategy is described here, which was designed with a cellular automata-based modeling and using a repulsive pheromone-based search. The environmental structure is well-known to all robots and their current positions are shared by the team. Besides, they communicate indirectly through the repulsive pheromone, which is available to each robot as an information about its neighborhood. The pheromone is deposited at each time step by each robot, over its current position and neighborhood cells. The pheromone is also evaporated as the time goes by. All next movement decisions are stochastic giving a non-deterministic characteristic to the model. Simulation results are presented, by applying the proposed model to different environmental conditions.
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References
Kıran, M.S., Gündüz, M., Baykan, Ö.K.: A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimum. Appl. Math. Comput. 219(4), 1515–1521 (2012)
Ferreira, G.B.S., Vargas, P.A., Oliveira, G.M.B.: An improved cellular automata-based model for robot path-planning. In: Mistry, M., Leonardis, A., Witkowski, M., Melhuish, C. (eds.) TAROS 2014. LNCS, vol. 8717, pp. 25–36. Springer, Heidelberg (2014)
Behring, C., Bracho, M., Castro, M., Moreno, J.: An algorithm for robot path planning with cellular automata. In: Bandini, S., Worsch, T. (eds.) Theory and Practical Issues on Cellular Automata, pp. 11–19. Springer, Heidelberg (2001)
Biswas, S., Das, S., Debchoudhury, S., Kundu, S.: Co-evolving bee colonies by forager migration: a multi-swarm based Artificial Bee Colony algorithm for global search space. Appl. Math. Comput. 232, 216–234 (2014)
Quang, N.N., Sanseverino, E.R., Di Silvestre, M.L., Madonia, A., Li, C., Guerrero, J.M.: Optimal power flow based on glow worm-swarm optimization for three-phase islanded microgrids. In: AEIT Annual Conference-From Research to Industry, pp. 1–6. IEEE (2014)
Ioannidis, K., Sirakoulis, G.C., Andreadis, I.: Cellular ants: a method to create collision free trajectories for a cooperative robot team. Rob. Auton. Syst. 59(2), 113–127 (2011)
Calvo, R., de Oliveira, J.R., Figueiredo, M., Romero, R.A.F.: Bio-inspired coordination of multiple robots systems, stigmergy mechanims to cooperative exploration, surveillance tasks. In: 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS), pp. 223–228. IEEE (2011)
Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A. (eds.): Ant Colony Optimization and Swarm Intelligence, vol. 5217. Springer, Heidelberg (2008)
Yingying, D., Yan, H., Jingping, J.: Multi-robot cooperation method based on the ant algorithm. In: Proceedings of the 2003 IEEE on Swarm Intelligence Symposium, SIS 2003, pp. 14–18. IEEE (2003)
Castello, E., Yamamoto, T., Libera, F.D., Liu, W., Winfield, A.F., Nakamura, Y., Ishiguro, H.: Adaptive foraging for simulated, real robotic swarms: the dynamical response threshold approach. Swarm Intell. 10(1), 1–31 (2016)
Kantor, G., Singh, S., Peterson, R., Rus, D., Das, A., Kumar, V., Pereira, G., Spletzer, J.: Distributed search, rescue with robot, sensor teams. In: Yuta, S., Asama, H., Prassler, E., Tsubouchi, T., Thrun, S. (eds.) Field and Service Robotics, pp. 529–538. Springer, Heidelberg (2006)
Saska, M., Vakula, J., Preucil, L.: Swarms of micro aerial vehicles stabilized under a visual relative localization. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 3570–3575. IEEE (2014)
Calvo, R., de Oliveira, J.R., Romero, R.A., Figueiredo, M.: A bioinspired coordination strategy for controlling of multiple robots in surveillance tasks. Int. J. Adv. Softw. 5(3 & 4), 146–165 (2012)
Falleiros, E.L.S., Calvo, R., Ishii, R.P.: PheroSLAM: a collaborative and bioinspired multi-agent system based on monocular vision. In: Gervasi, O., Murgante, B., Misra, S., Gavrilova, M.L., Rocha, A.M.A.C., Torre, C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2015. LNCS, vol. 9156, pp. 71–85. Springer, Heidelberg (2015)
Wei-Guo, S., Yan-Fei, Y., Bing-Hong, W., Wei-Cheng, F.: Evacuation behaviors at exit in CA model with force essentials: a comparison with social force model. Physica A: Stat. Mech. Appl. 371(2), 658–666 (2006)
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Lima, D.A., Tinoco, C.R., Oliveira, G.M.B. (2016). A Cellular Automata Model with Repulsive Pheromone for Swarm Robotics in Surveillance. In: El Yacoubi, S., Wąs, J., Bandini, S. (eds) Cellular Automata. ACRI 2016. Lecture Notes in Computer Science(), vol 9863. Springer, Cham. https://doi.org/10.1007/978-3-319-44365-2_31
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DOI: https://doi.org/10.1007/978-3-319-44365-2_31
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