Abstract
In nature, in order to hunt prey more efficiently, animals often adopt the method of group cooperation. By analyzing the similarity between the behavior of biological clusters and the control of unmanned aerial vehicle (UAV) clusters, this paper proposes a distributed hunting algorithm based on the hunting behavior of wolves to solve the target hunting problem in the cooperative combat of UAV clusters. Firstly, the chase and escape model of UAV is established, and the escape mode of the target is designed according to the artificial potential field method. Secondly, the hunting strategy of UAV clusters is determined by imitating the behavior characteristics of wolves during hunting. Finally, it is verified by simulation experiment. In this paper, the simulation hunting environment and multi-agent reinforcement learning environment are respectively verified, the UAV clusters can realize the target hunting, which proves the effectiveness of the algorithm.
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
Nikolic, J., Leutenegger, S., Burri, M., et al.: A UAV System for Inspection of Industrial Facilities[C]. In: 2013 IEEE Aerospace Conference. [V.4].:Institute of Electrical and Electronics Engineers, pp. 2638–2645 (2013)
Duan, H., Pei, L.I.: Autonomous control for unmanned aerial vehicle swarms based on biological collective behaviors. Sci. Technol. Rev. (2017)
Kendra, Lt., Cook, L.B.: The institute of electrical and electronics engineers,inc. the silent force multiplier: the history and role of UAVs in warfare. In: 2007 IEEE Aerospace Conference, vol. 7(9), pp. 3194–3200 (2007)
Huang, C.: Research on key technology of future air combat process Intelligentization. Aero Weaponry (2019)
Li, J., Pan, Q., Hong, B.: A new approach of multi-robot cooperative pursuit based on association rule data mining. Int. J. Adv. Robotic Syst. (2009)
Uehara, S., Takimoto, M., Kambayashi, Y.: Mobile Agent Based Obstacle Avoidance in Multi-robot Hunting. Springer International Publishing (2017). Doi: https://doi.org/10.1007/978-3-319-49049-6_32
Luan, X., Sun., Y.: Research on cooperative encirclement strategy of multiple underwater robots based on wolf swarm algorithm. J. Phys. Conf. Ser. 1570(1), 012017 (6pp) (2020)
Dutta, K.: Hunting in groups. Resonance 19(10), 936–957 (2014)
Cai, Y.F., Tang, Z.M., Zhang, H.F:. Multi-robots cooperative hunting strategy based on Cross-EKF localization. Kongzhi yu Juece/Control Decision 25(9), 1313–1317+1323 (2010)
An, Y., Li, S., Da, L.: Multiple Robotic Fish's Target Search and Cooperative Hunting Strategies. TELKOMNIKA Indonesian J. Electrical Eng. 12(1) (2014)
Chen, S., Chen, X., Mei, Y., et al.: A cooperative hunting algorithm of multi-robot based on dynamic prediction of the target via consensus-based kalman filtering. Journal of Information and Computational Science 12(4), 1557–1568 (2015)
Choi, S., Kim, J.: Three dimensional formation control to pursue an underwater evader utilizing underwater robots measuring the sound generated from the evader. IEEE Access 7, 150720–150728 (2019)
Jin, L., Li, S., La, H.M., et al.: Dynamic task allocation in multi-robot coordination for moving target tracking: A distributed approach. Automatica 100, 75–81 (2019)
Xum B., Zhang, H.T., Meng, H., et al.: Moving target surrounding control of linear multiagent systems with input saturation. IEEE Trans. Syst. Man Cybern. Syst. (2020)
Dou, L., Song, C., Wang, X., et al.: Target localization and enclosing control for networked mobile agents with bearing measurements. Automatica 118, 109022 (2020)
De Souza, C., Newbury, R., Cosgun, A., et al.: Decentralized multi-agent pursuit using deep reinforcement learning. IEEE Robot. Automat. Lett. 6(3), 4552–4559 (2021)
Palmer, G., Tuyls, K., Bloembergen, D., et al.: Lenient multi-agent deep reinforcement learning. arXiv preprint arXiv:1707.04402 (2017)
Barton, S.L., Waytowich, N.R., Zaroukian, E., et al.: Measuring collaborative emergent behavior in multi-agent reinforcement learning. In: International Conference on Human Systems Engineering and Design: Future Trends and Applications. Springer, Cham, pp.  422–427 (2018)
Hu, J., Turgut, A.E., Lennox, B., et al.: Robust formation coordination of robot swarms with nonlinear dynamics and unknown disturbances: Design and experiments. IEEE Trans. Circuits Syst. II Express Briefs 69(1), 114–118 (2021)
Zhang, P., Xue, H., Gao, S., et al.: Distributed adaptive consensus tracking control for multi-agent system with communication constraints. IEEE Trans. Parallel Distrib. Syst. 32(6), 1293–1306 (2020)
Tacchetti, A., Song, H.F., Mediano, P.A.M., et al.: Relational forward models for multi-agent learning. arXiv preprint arXiv:1809.11044 (2018)
Muro, C., Escobedo, R., Spector, L., et al.: Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations. Behav. Proc. 88(3), 192–197 (2011)
Lowe, R., Wu, Y., Tamar, A., et al.: Multi-agent actor-critic for mixed cooperative-competitive environments. In: Proceedingsof the Advances in Neural Information Processing Systems, pp. 6379 - 6390 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, T., Wang, J., Ouyang, M., Tai, Y. (2023). UAV Target Roundup Strategy Based on Wolf Pack Hunting Behavior. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1681. Springer, Singapore. https://doi.org/10.1007/978-981-99-2356-4_40
Download citation
DOI: https://doi.org/10.1007/978-981-99-2356-4_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-2355-7
Online ISBN: 978-981-99-2356-4
eBook Packages: Computer ScienceComputer Science (R0)
