Abstract
In this paper, considering the challenge of gas pollution prevention and the development of unmanned system technology, a type of pollution source detection algorithm which utilizes unmanned aerial vehicle is investigated. The whole research is based on turbulence gas diffusion model derived from Fick’ s laws of diffusion, aiming to determine the location of gas pollution source in a certain area by using drone cluster to measure gas concentration in each sampling point. Firstly, based on the gas diffusion model, this paper proposes a gas pollution source estimation algorithm to provide the target detection for the algorithm. Then, in pursuit of higher execution efficiency and to reduce the risk of collision, an optimized path planning algorithm and an obstacle avoidance algorithm are designed. These algorithms are integrated as a whole, guiding drone cluster to continuously approach the gas pollution sources through multiple rounds of tasks with optimal paths. Finally, simulation is provided to test the whole algorithm, displaying the validity of this algorithm.
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References
Air Pollution. https://www.who.int/health-topics/air-pollution#tab=tab_1. Accessed 12 June 2023
World Health Organization. WHO Ambient Air Quality Database, 2022 Update: Status Report. Department of Environment, Climate Change and Health World Health Organization, Switzerland (2023)
Rani, V.K., Vallikanna, A.L.: Air pollution monitoring system using internet of vehicles and pollution sensors. In: 2020 4th International Conference on Electronics. Communication and Aerospace Technology (ICECA), LNCS, pp. 249–252. Coimbatore, India (2020)
Jiyal, S., Sheetlani, J., Saini, R.K.: Internet of Things: A Survey on Air Pollution Monitoring, Challenges and Applications. In: 2022 7th International Conference on Computing, Communication and Security (ICCCS), Seoul. LNCS, pp. 1–6 (2022)
Chen, Y., Chen, D., Song, T., Song, K.: An intelligent and portable air pollution monitoring system based on chemical sensor array. In: 2020 IEEE 4th International Conference on Frontiers of Sensors Technologies (ICFST), Shanghai. LNCS, pp. 21–25 (2020)
Jin, Z.: Scientific and Technological Innovation. 30, 21–24 (2022)
Belkhiri, A., Bechkit, W., Rivano, H.: Virtual forces based UAV fleet mobility models for air pollution monitoring. In: 2018 IEEE 43rd Conference on Local Computer Networks (LCN), Chicago. LNCS, pp. 481–484 (2018)
Zhang, Q., Lu, S., Zhang, J., Jiang, Z., Li, Z.: Path planning of inspection robot integrated safety A* algorithm and improved artificial potential field method. China Acad. J. Electron. Publish. House 1006–8228 (2022)
Kong, H., Xia, L., Zhang, Q.: Path planning for intelligent vehicle collision avoidance based on improved artificial potential field method. J. Hefei Univ. Technol. 46(5)
Yu, X., Jiang, C., Duan, S., Deng, Q.: Improving path planning with A* algorithm and APF algorithm. J. Syst. Simulat.
Melehy, M.A.: Diffusion forces and diffusion potentials. Proc. IEEE 51(7), 1028 (1963)
Zhang, M., Chen, X.: Weighted centroid localization algorithm based on gas leakage source with wind. Electron. Design Eng. 27(8), 54–62 (2019)
Singh, C., Jirutitijaroen, P., Mitra, J.: Electric Power Grid Reliability Evaluation: Models and Methods. Wiley-IEEE Press, New York (2019)
Rakesh, N., Shaneeth, M., Jayaraj, S.: One dimensional modelling of oxygen diffusion in a gas diffusion layer of PEM Fuel Cell. In: 2012 International Conference on Green Technologies (ICGT), Trivandrum, pp. 340–342 (2012)
Ding, J., Du, C., Zhao, Y., Yin, D.: Path planning algorithm for unmanned aerial vehicles based on improved artificial potential field. J. Comput. Appl. 36(1), 287–290 (2016)
An, L., Chen, T., Cheng, A., Fang, W.: A simulation on the path planning of intelligent vehicles based on artificial potential field algorithm. Automot. Eng. 1(39), 12 (2017)
Lee, J., Lee, Y., Kim, H.: Decision of error tolerance in array element by the Monte Carlo method. IEEE Trans. Antennas Propag. 53(4), 1325–1331 (2005)
Acknowledgments
The authors would like to express sincere gratitude for the valuable assistance and insightful advice provided by Tianxian Zhang, Hang Su, Haisheng Cheng, Zicheng Wang.
The authors declare that this work does not have any conflicts of interest.
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Wang, Y., Wang, T., Liao, L., Zhang, T. (2024). Fully Automated UAV Cluster Pollution Source Detection Algorithm. In: Qu, Y., Gu, M., Niu, Y., Fu, W. (eds) Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023). ICAUS 2023. Lecture Notes in Electrical Engineering, vol 1174. Springer, Singapore. https://doi.org/10.1007/978-981-97-1091-1_17
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DOI: https://doi.org/10.1007/978-981-97-1091-1_17
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