Abstract
In response to the increasing severity and frequency of forest fires caused by dry climate and temperature rise, and the difficulty of identifying the fire initiation point in the early stage due to the large forest area, an effective method of utilizing unmanned aerial vehicles (UAVs) for early fire search and extinguishing is used in this article. This is a path planning problem in multi-agent multi-objective search in uncertain environments. This article proposes an improved ant colony algorithm based on the idea of competition cooperation. Based on the advantages of ant colony algorithm in path planning, exclusion attraction pheromones are added to the original algorithm, which enhances the detection area and fire extinguishing speed. On the NetLogo simulation platform, by comparing the convergence time and coverage area of the improved ant colony algorithm with the random walk algorithm and ant colony algorithm, it is found that the detection performance of the improved ant colony algorithm has significantly improved. The proposed UAVs based forest wildfire detection model based on improved ant colony algorithm is of great significance for the suppression of forest fires.
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References
McAlpine, R.S., Wotton, B.M.: The use of fractal dimension to improve wildland fire perimeter predictions. Can. J. For. Res. 23(6), 1073–1077 (1993)
Martinez-de Dios, J.R., Arrue, B.C., Ollero, A., et al.: Computer vision techniques for forest fire perception. Image Vis. Comput. 26(4), 550–562 (2008)
Singh, R., Gehlot, A., Akram, S.V., Thakur, A.K., Buddhi, D., Das, P.K.: Forest 4.0: digitalization of forest using the Internet of Things (IoT). J. King Saud Univ. Comput. Inf. Sc. 34(8), 5587–5601 (2022). https://doi.org/10.1016/j.jksuci.2021.02.009
Akhloufi, M.A., Couturier, A., Castro, N.A.: Unmanned aerial vehicles for wildland fires: sensing, perception, cooperation and assistance. Drones 5(1), 15 (2021)
Cummings, M.L., Clare, A., Hart, C.: The role of human-automation consensus in multiple unmanned vehicle scheduling. Hum. Factors 52(1), 17–27 (2010)
Zhang, H., Xin, B., Dou, L., et al.: A review of cooperative path planning of an unmanned aerial vehicle group. Front. Inf. Technol. Elect. Eng. 21(12), 1671–1694 (2020)
Li, J., Xiong, Y., She, J.: An improved ant colony optimization for path planning with multiple UAVs. In: 2021 IEEE International Conference on Mechatronics (ICM), pp. 1–5. IEEE (2021)
Zhen, Z., Xing, D., Gao, C.: Cooperative search-attack mission planning for multi-UAV based on intelligent self-organized algorithm. Aerosp. Sci. Technol. 76, 402–411 (2018)
Shivgan, R., Dong, Z.: Energy-efficient drone coverage path planning using genetic algorithm. In: 2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR), pp. 1–6. IEEE (2020)
Dhall, A., Dhasade, A., Nalwade, A., et al.: A survey on systematic approaches in managing forest fires. Appl. Geogr. 121, 102266 (2020)
Netlogo: Netlogo. Accessed September 2017 (2017). http://ccl.northwestern.edu/netlogo/
Tisue, S., Wilensky, U.: Center for connected learning and computer-based modeling northwestern University, Evanston, Illinois. In: NetLogo: A Simple Environment for Modeling Complexity, Citeseer (1999)
Dong, W., Zhou, M.C.: A supervised learning and control method to improve particle swarm optimization algorithms. IEEE Trans. Syst. Man Cybern. Syst. 47(7), 1135–1148 (2016)
Bobkov, S.P., Astrakhantseva, I.A.: The use of multi-agent systems for modeling technological processes. J. Phys. Conf. Ser. 2001(1), 012002 (2021)
Yue, W., Xi, Y., Guan, X.: A new searching approach using improved multi-ant colony scheme for multi-UAVs in unknown environments. IEEE Access 7, 161094–161102 (2019)
Zhang, Y., Wang, S., Ji, G.: A rule-based model for bankruptcy prediction based on an improved genetic ant colony algorithm. Math. Probl. Eng. 2013(pt.14), 1–10 (2013)
Zhang, Y., Wu, L.: Weights optimization of neural network via improved BCO approach. Progr. Electromagn. Res. 83(5), 185–198 (2008)
Cekmez, U., Ozsiginan, M., Sahingoz, O.K.: Multi-UAV path planning with multi colony ant optimization. In: Abraham, A., Kr, P., Muhuri, A.K., Muda, N.G. (eds.) Intelligent Systems Design and Applications, pp. 407–417. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-76348-4_40
Kumar, A., Wu, S., Huang, Y., et al.: Mercury from wildfires: global emission inventories and sensitivity to 2000–2050 global change. Atmos. Environ. 173, 6–15 (2018)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (grant number 41972111) and the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (grant number 2019QZKK020604).
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Xue, F., Geng, P., Hao, H., He, Y., Liang, H. (2024). A Multiple Fire Zones Detection Method for UAVs Based on Improved Ant Colony Algorithm. In: Jin, H., Pan, Y., Lu, J. (eds) Data Science and Information Security. IAIC 2023. Communications in Computer and Information Science, vol 2059. Springer, Singapore. https://doi.org/10.1007/978-981-97-1280-9_15
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DOI: https://doi.org/10.1007/978-981-97-1280-9_15
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