Abstract
In recent years, with the application and gradual popularization of UAV technology in many fields, the normalization of UAV aerial photography has become a common phenomenon. There are few studies on Multi-UAV regional coverage monitoring. In this study, PSO algorithm based on pruning can be used to solve optimization problem, the multi-agent technology solve the problem of UAV group for self-perception and decision-making, the UAV group, namely multi-agent particles, can automatically initialize the perception agent particle whether meet the requirements of the iteration, whether to need to be pruned, and decide to optimize the iteration or increase the agent number, Finally, simulation verification shows that the optimized algorithm can finish the work faster under the premise of guaranteeing the effect.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bouvry, P., Chaumette, S., Danoy, G., et al.: Using heterogeneous multilevel swarms of UAVs and high-level data fusion to support situation management in surveillance scenarios
Wietfeld, C., Kai, D.: Cognitive Networking for UAV Swarms. Springer, Netherlands (2015)
Nasiri, J.A., Fard, A.M., Naghibzadeh, M., et al.: High dimensional problem optimization using distributed multi-agent PSO. In: Third Uksim European Symposium on Computer Modeling and Simulation, pp. 245–250. IEEE Computer Society (2009)
Lin, Z., Chen, Z., Fu, M.: A linear control approach to distributed multi-agent formations in d-dimensional space. In: Decision and Control, pp. 6049–6054. IEEE (2013)
Littman, M.L.: Markov games as a framework for multi-agent reinforcement learning. In: Machine Learning Proceedings, pp. 157–163 (1994)
Hasegawa, K., Noto, M.: Swarm intelligence algorithm for optimality discovery in distributed constraint optimization. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 3611–3616. IEEE (2014)
Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proc. IEEE 95(1), 215–233 (2007)
Zhou, Y., Wang, J., Yin, J.: Genetic particle swarm optimization based on multiagent model for combinatorial optimization problem (2008)
Afaq, H., Saini, S.: Swarm intelligence based soft computing techniques for the solutions to multiobjective optimization problems. Int. J. Comput. Sci. Issues 8(3), 498 (2011)
Sakti, A.M., Jovan, F., Rys, D., et al.: Modified PSO algorithm for odor source localization problems: progress and challenge. Cloud.snappages.com
Frans, V.D.B., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 225–239 (2004)
Kumar, V., Minz, S., Kumar, V., et al.: An optimal multi-view ensemble learning for high dimensional data classification using constrained particle swarm optimization. In: Information, Communication and Computing Technology (2017)
Chang, F.-C., Huang, H.-C.: A Survey on Intelligent Sensor Network and Its Applications. J. Netw. Intell. 1(1), 1–15 (2016)
Wang, Z., Chen, K., He, L.: AsySIM: modeling asymmetric social influence for rating prediction. Data Sci. Pattern Recognit. 2(1), 25–40 (2018)
Acknowledgements
This study was supported by the Research Program Foundation of Minjiang University under Grants No. MYK17021 and supported by the Major Project of Sichuan Province Key Laboratory of Digital Media Art under Grants No. 17DMAKL01 and supported by Fujian Province Guiding Project under Grants No. 2018H0028.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, K., Shen, Y., Zhang, F., Liang, Z., Song, Z., Pan, Y. (2019). Regional Coverage Monitoring Planning Technology for Multi-UAV Based on Pruning PSO. In: Zhao, Y., Wu, TY., Chang, TH., Pan, JS., Jain, L. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2018. Smart Innovation, Systems and Technologies, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-030-04585-2_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-04585-2_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04584-5
Online ISBN: 978-3-030-04585-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)