Swarm Robotics surveillance control with Ant Cellular Automata Model in the Cerrado Biome for preserving biodiversity

Authors

DOI:

https://doi.org/10.5753/jis.2024.3796

Keywords:

Swarm Robotics, Cellular Automata, Inverted Ants Pheromone, Environmental Surveillance, Sete Cidades National Park, Cerrado Biome, Interactive Patrol, Geographic Information System

Abstract

The Cerrado biome in Brazil plays a vital role in preserving biodiversity, providing essential ecosystem services, and supporting agriculture, making it a crucial and valuable natural resource. Sete Cidades National Park stands out for its rock formations, 10,000-year-old cave paintings and its Cerrado vegetation. The Cerrado is known for being a pyrobiome, so its patrolling becomes essential. In this context, this paper introduces a novel approach to swarm robotics patrolling in the unique ecosystem of the Sete Cidades National Park, located within the Cerrado biome. The study presents three distinct cellular automata models designed for the task, aiming to enhance the efficiency and coverage of patrolling efforts. The key difference between our model presented in this research and the previous one is our focus on a map that encompasses diverse vegetation types, specifically designed to represent the Sete Cidades National Park, with a primary goal of monitoring forest fires. The results demonstrated that the best-performing model was the Forest Tabu Inverted Ant Cellular Automata, which achieved an average of 21.77 complete patrol cycles with 95% confidence. This outcome was obtained using three robots, a tabu queue of |Q| = 80, and a maximum pheromone per cell equals to ρ = 103. These parameters highlight the efficacy of this model in optimizing patrol cycles and the efficient use of resources for environmental surveillance in the Sete Cidades National Park, particularly in the context of fire prevention.

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References

Alencar, J., Cordeiro, W. P. F. d. S., Staples, G., and Buril, M. T. (2019). Convolvulaceae no parque nacional de sete cidades, estado do piauí, brasil. Hoehnea, 46:e992018. DOI: https://doi.org/10.1590/2236-8906-99/2018.

Alexan, W., ElBeltagy, M., and Aboshousha, A. (2022). Rgb image encryption through cellular automata, s-box and the lorenz system. Symmetry, 14(3):443. DOI: https://doi.org/10.3390/sym14030443.

Alvarado, S., Carvalho, I., Ferraz, T., and Silva, T. (2019). Effects of fire suppression policies on fire regimes in protected areas in the cerrado. Biodiversidade Brasileira-BioBrasil, (1). DOI: https://doi.org/10.37002/biodiversidadebrasileira.v9i1.1143.

Brasiel, H. C. and Lima, D. A. (2023). Exploring the influence of wind, vegetation and water sources on the spread of forest fires in the brazilian cerrado biome using cellular automata. In Anais do XIV Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais, pages 61–70. SBC. DOI: https://doi.org/10.5753/wcama.2023.230476.

Calvo, R., de Oliveira, J. R., Figueiredo, M., and Romero, R. A. (2014). Parametric investigation of a distributed strategy for multiple agents systems applied to cooperative tasks. In Proceedings of the 29th Annual ACM Symposium on Applied Computing, pages 207–212. ACM. DOI: https://doi.org/10.1145/2554850.2554977.

Castello, E., Yamamoto, T., Dalla Libera, F., Liu, W., Winfield, A. F., Nakamura, Y., and Ishiguro, H. (2016). Adaptive foraging for simulated and real robotic swarms: the dynamical response threshold approach. Swarm Intelligence, pages 1–31. DOI: https://doi.org/10.1007/s11721-015-0117-7.

Dorigo, M., Birattari, M., and Stutzle, T. (2006). Ant colony optimization. IEEE computational intelligence magazine, 1(4):28–39. DOI: https://doi.org/10.1109/MCI.2006.329691.

Ferreira, M. E. A., Lima, D. A., Martins, L. G., and Oliveira, G. M. (2022a). Refining a parameter setting evolutionary approach for fire spreading models based on cellular automata. In 2022 International Conference on Computational Science and Computational Intelligence (CSCI), pages 480–486. IEEE. DOI: https://doi.org/10.1109/CSCI58124.2022.00091.

Ferreira, M. E. A., Quinta, A. L., Lima, D. A., Martins, L. G., and Oliveira, G. (2022b). Automatic evolutionary adjustment of cellular automata model for forest fire propagation. In International Conference on Cellular Automata for Research and Industry, pages 235–245. Springer. DOI: https://doi.org/10.1007/978-3-031-14926-9_21.

Gaia, J. A. S., Souza, B. I. d., Lucena, R. F. P. d., Souza, R. S., and Gaia, C. L. B. (2022). Modelagem e distribuição potencial de espécies arbóreas relevantes para a dinâmica sociocultural e ecológica do parque nacional de sete cidades, piauí, brasil. Sociedade & Natureza, 32:784–798. DOI: https://doi.org/10.14393/SN-v32-2020-51103.

Gharaibeh, A., Shaamala, A., Obeidat, R., and Al-Kofahi, S. (2020). Improving land-use change modeling by integrating ann with cellular automata-markov chain model. Heliyon, 6(9). DOI: https://doi.org/10.1016/j.heliyon.2020.e05092.

Glover, F. (1989). Tabu search part i. ORSA Journal on computing, 1(3):190–206. DOI: https://doi.org/10.1287/ijoc.1.3.190.

Glover, F. (1990). Tabu search part ii. ORSA Journal on computing, 2(1):4–32. DOI: https://doi.org/10.1287/ijoc.2.1.4.

Horibe, K., Walker, K., and Risi, S. (2021). Regenerating soft robots through neural cellular automata. In Genetic Programming: 24th European Conference, EuroGP 2021, Held as Part of EvoStar 2021, Virtual Event, April 7–9, 2021, Proceedings 24, pages 36–50. Springer. DOI: https://doi.org/10.1007/978-3-030-72812-0_3.

Ioannidis, K., Sirakoulis, G. C., and Andreadis, I. (2011). A path planning method based on cellular automata for cooperative robots. Applied Artificial Intelligence, 25(8):721–745. DOI: https://doi.org/10.1080/08839514.2011.606767.

Lima, D. A. and Oliveira, G. M. (2017). A cellular automata ant memory model of foraging in a swarm of robots. Applied Mathematical Modelling, 47:551–572. DOI: https://doi.org/10.1016/j.apm.2017.03.021.

Lima, D. A., Tinoco, C. R., and Oliveira, G. M. B. (2016). A cellular automata model with repulsive pheromone for swarm robotics in surveillance. In Cellular Automata - International Conference on Cellular Automata for Research and Industry, ACRI. Proceedings, pages 312–322. DOI: https://doi.org/10.1007/978-3-319-44365-2_31.

Lima, H. A. and Lima, D. A. (2014). Autômatos celulares estocásticos bidimensionais aplicados a simulação de propagação de incêndios em florestas homogêneas. In Anais do V Workshop de Computação Aplicada a Gestão do Meio Ambiente e Recursos Naturais, pages 15–24. SBC. DOI: https://doi.org/10.13140/RG.2.1.4578.8564.

Lopes, H. J. and Lima, D. A. (2021). Evolutionary tabu inverted ant cellular automata with elitist inertia for swarm robotics as surrogate method in surveillance task using e-puck architecture. Robotics and Autonomous Systems, page 103840. DOI: https://doi.org/10.1016/j.robot.2021.103840.

Lopes, H. J. and Lima, D. A. (2022). Surveillance task optimized by evolutionary shared tabu inverted ant cellular automata model for swarm robotics navigation control. Results in Control and Optimization, 8:100141. DOI: https://doi.org/10.1016/j.rico.2022.100141.

Matos, M. d. Q. and Felfili, J. M. (2010). Florística, fitossociologia e diversidade da vegetação arbórea nas matas de galeria do parque nacional de sete cidades (pnsc), piauí, brasil. Acta botânica brasílica, 24:483–496. DOI: https://doi.org/10.1590/S0102-33062010000200019.

Monteiro, L., Fanti, V., and Tessaro, A. (2020). On the spread of sars-cov-2 under quarantine: A study based on probabilistic cellular automaton. Ecological Complexity, 44:100879. DOI: https://doi.org/10.1016/j.ecocom.2020.100879.

Mordvintsev, A., Randazzo, E., Niklasson, E., and Levin, M. (2020). Growing neural cellular automata. Distill, 5(2):e23. DOI: https://doi.org/10.23915/distill.00023.

Oliveira, M. E. A., Martins, F. R., Castro, A., and Santos, J. d. (2007). Classes de cobertura vegetal do parque nacional de sete cidades (transição campo-floresta) utilizando imagens tm/landsat, ne do brasil. XIII Simpósio Brasileiro de Sensoriamento Remoto, 13.

Rodrigues, L. G. S., Dias, D. R. C., de Paiva Guimarães, M., Brandão, A. F., Rocha, L. C., Iope, R. L., and Brega, J. R. F. (2022). Supervised classification of motor-rehabilitation body movements with rgb cameras and pose tracking data. Journal on Interactive Systems, 13(1):221– 231. DOI: https://doi.org/10.5753/jis.2022.2409.

Sanches, S. R., Oizumi, M. A., Oliveira, C., Sementille, A. C., and Corrêa, C. G. (2019). The influence of the device on user performance in handheld augmented reality. Journal on Interactive Systems, 10(1). DOI: https://doi.org/10.5753/jis.2019.718.

Souza, N. L. B. and Lima, D. A. (2019). Tabu search for the surveillance task optimization of a robot controlled by two-dimensional stochastic cellular automata ants model. In Latin American Robotics Symposium, Brazilian Symposium on Robotics and Workshop on Robotics in Education, pages 299– 304. IEEE. DOI: https://doi.org/10.1109/LARS-SBR-WRE48964.2019.00059.

Tinoco, C. R., Lima, D. A., and Oliveira, G. M. (2017). An improved model for swarm robotics in surveillance based on cellular automata and repulsive pheromone with discrete diffusion. International Journal of Parallel, Emergent and Distributed Systems, 34(1):53–77. DOI: https://doi.org/10.1080/17445760.2017.1334886.

Zeng, J., Qian, Y., Yin, F., Zhu, L., and Xu, D. (2022). A multi-value cellular automata model for multi-lane traffic flow under lagrange coordinate. Computational and Mathematical Organization Theory, pages 1–15. DOI: https://doi.org/10.1007/s10588-021-09345-w.

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Published

2024-04-20

How to Cite

BRASIEL, H. C.; LIMA, D. A. Swarm Robotics surveillance control with Ant Cellular Automata Model in the Cerrado Biome for preserving biodiversity. Journal on Interactive Systems, Porto Alegre, RS, v. 15, n. 1, p. 375–387, 2024. DOI: 10.5753/jis.2024.3796. Disponível em: https://journals-sol.sbc.org.br/index.php/jis/article/view/3796. Acesso em: 21 nov. 2024.

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Section

Regular Paper