Abstract
This work proposed the application of an evolutionary technique to optimise the parameters of a coordination model for swarms of robots. A genetic algorithm with standard characteristics was applied in order to find suitable parameters for the IACA-DI model (Inverted Ant Cellular Automata with Discrete pheromone diffusion and Inertial motion), which, in turn, was proposed in previous works. The IACA-DI is a model to coordinate swarms of robots based on the combination of two bio-inspired techniques: cellular automata and inverted ant system. The main purpose of the model is to carry out surveillance, exploration and foraging tasks. Experiments were performed in different configurations of environments and with different movement strategies to validate this application. Results have shown significant improvements in the model performance compared with previous empirical calibrations, granting a better understanding of the IACA-DI parameters, and allowing significant improvements to be investigated in future works.
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References
Bontzorlos, T., Sirakoulis, G.C.: Bioinspired algorithm for area surveillance using autonomous robots. Int. J. Parallel Emerg. Distrib. Syst. (IJPEDS) 32(4), 368–385 (2017)
Brambilla, M., Ferrante, E., et al.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013)
Byington, M.D., Bishop, B.E.: Cooperative robot swarm locomotion using genetic algorithms. In: SE Symposium on Systems Theory, pp. 252–256. IEEE (2008)
Calvo, R., Oliveira, J.R., Romero, R.A.F., Figueiredo, M.: A bioinspired coordination strategy for controlling of multiple robots in surveillance tasks. Int. J. Adv. Softw. 5, 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., et al. (eds.) ICCSA 2015. LNCS, vol. 9156, pp. 71–85. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21407-8_6
Lima, D.A., Oliveira, G.M.B.: A probabilistic cellular automata ant memory model for a swarm of foraging robots. In: International Conference on Control, Automation, Robotics and Vision, pp. 1–6. IEEE (2016)
Lima, D.A., Tinoco, C.R., Oliveira, G.M.B.: A cellular automata model with repulsive pheromone for swarm robotics in surveillance. In: El Yacoubi, S., Wąs, J., Bandini, S. (eds.) ACRI 2016. LNCS, vol. 9863, pp. 312–322. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44365-2_31
Lin, C.C., Chen, K.C., Chuang, W.J.: Motion planning using a memetic evolution algorithm for swarm robots. Int. J. Adv. Robot. Syst. 9(1), 19 (2012)
Ludwig, L., Gini, M.: Robotic swarm dispersion using wireless intensity signals. In: Gini, M., Voyles, R. (eds.) Distributed Autonomous Robotic Systems, pp. 135–144. Springer, Tokyo (2006). https://doi.org/10.1007/4-431-35881-1_14
Nakano, R.C.S., Bandala, A., Faelden, G.E., et al.: A genetic algorithm approach to swarm centroid tracking in quadrotor unmanned aerial vehicles. In: International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pp. 1–6. IEEE (2014)
Oliveira, G.M.B., Silva, R., Amaral, L., Martins, L.G.: An evolutionary-cooperative model based on cellular automata and genetic algorithms for the navigation of robots under formation control. In: Brazilian Conference on Intelligent Systems (BRACIS), pp. 426–431. IEEE (2018)
Pandey, A., Pandey, S., Parhi, D.R.: Mobile robot navigation and obstacle avoidance techniques: a review. Int. Rob. Auto J. 2(3), 00022 (2017)
Rezk, N.M., Alkabani, Y., Bedor, H., Hammad, S.: A distributed genetic algorithm for swarm robots obstacle avoidance. In: 2014 9th International Conference on Computer Engineering & Systems (ICCES), pp. 170–174. IEEE (2014)
Saska, M., Vakula, J., Přeućil, L.: Swarms of micro aerial vehicles stabilized under a visual relative localization. In: International Conference on Robotics and Automation (ICRA), pp. 3570–3575. IEEE (2014)
Soysal, O., Bahçeci, E., Şahin, E.: Aggregation in swarm robotic systems: evolution and probabilistic control. Turk. J. Electr. Eng. Comput. Sci. 15(2), 199–225 (2007)
Tinoco, C.R., Lima, D.A., Oliveira, G.M.B.: An improved model for swarm robotics in surveillance based on cellular automata and repulsive pheromone with discrete diffusion. Int. J. Parallel Emerg. Distrib. Syst. 34, 53–77 (2017)
Tinoco, C.R., Oliveira, G.M.B.: Pheromone interactions in a cellular automata-based model for surveillance robots. In: Mauri, G., El Yacoubi, S., Dennunzio, A., Nishinari, K., Manzoni, L. (eds.) ACRI 2018. LNCS, vol. 11115, pp. 154–165. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99813-8_14
Tinoco, C.R., Oliveira, G.M.B.: Heterogeneous teams of robots using a coordinating model for surveillance task based on cellular automata and repulsive pheromone. In: Congress on Evolutionary Computation (CEC), pp. 747–754. IEEE (2019)
Vicmudo, M.P., Dadios, E.P., Vicerra, R.R.P.: Path planning of underwater swarm robots using genetic algorithm. In: International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, pp. 1–5. IEEE (2014)
Weyns, D., Omicini, A., Odell, J.: Environment as a first class abstraction in multiagent systems. Auton. Agents Multi Agent Syst. 14(1), 5–30 (2007)
Zheng, Z., Tan, Y.: Group explosion strategy for searching multiple targets using swarm robotic. In: Congress on Evolutionary Computation, pp. 821–828. IEEE (2013)
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Authors are grateful to FAPEMIG, CNPq and CAPES support and scholarships.
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Tinoco, C.R., Vizzari, G., Oliveira, G.M.B. (2021). Parameter Adjustment of a Bio-Inspired Coordination Model for Swarm Robotics Using Evolutionary Optimisation. In: Gwizdałła, T.M., Manzoni, L., Sirakoulis, G.C., Bandini, S., Podlaski, K. (eds) Cellular Automata. ACRI 2020. Lecture Notes in Computer Science(), vol 12599. Springer, Cham. https://doi.org/10.1007/978-3-030-69480-7_15
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