Autonomous Maneuver Decision-Making of UCAV with Incomplete Information in Human-Computer Gaming

  1. Li, Shouyi 1
  2. Wu, Qingxian 1
  3. Du, Bin 1
  4. Wang, Yuhui 1
  5. Chen, Mou 1
  6. González Aguilera, Diego 2
  1. 1 College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
  2. 2 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Revista:
Drones

ISSN: 2504-446X

Año de publicación: 2023

Volumen: 7

Número: 3

Páginas: 157

Tipo: Artículo

DOI: 10.3390/DRONES7030157 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Drones

Resumen

In human-computer gaming scenarios, the autonomous decision-making problem of an unmanned combat air vehicle (UCAV) is a complex sequential decision-making problem involving multiple decision-makers. In this paper, an autonomous maneuver decision-making method for UCAV that considers the partially observable states of Human (the adversary) is proposed, building on a game-theoretic approach. The maneuver decision-making process within the current time horizon is modeled as a game of Human and UCAV, which significantly reduces the computational complexity of the entire decision-making process. In each established game decision-making model, an improved maneuver library that contains all possible maneuvers (called the continuous maneuver library) is designed, and each of these maneuvers corresponds to a mixed strategy of the established game. In addition, the unobservable states of Human are predicted via the Nash equilibrium strategy of the previous decision-making stage. Finally, the effectiveness of the proposed method is verified by some adversarial experiments.

€ Ver financiación

Información de financiación

Financiadores

  • Major Projects for Science and Technology Innovation 2030
    • 2018AAA0100805

Referencias bibliográficas

  • Demir, (2021), Def. Technol., 17, pp. 657, 10.1016/j.dt.2020.01.005
  • Yang, (2022), Def. Technol., 18, pp. 1349, 10.1016/j.dt.2022.02.001
  • Li, S., Wu, Q., Chen, M., and Wang, Y. (2020). CISC 2020: Proceedings of 2020 Chinese Intelligent Systems Conference, Springer.
  • Guo, J., Wang, L., and Wang, X. (2022). A Group Maintenance Method of Drone Swarm Considering System Mission Reliability. Drones, 6.
  • Shin, (2018), Aerosp. Sci. Technol., 72, pp. 305, 10.1016/j.ast.2017.11.014
  • Li, J., Chen, R., and Peng, T. (2022). A Distributed Task Rescheduling Method for UAV Swarms Using Local Task Reordering and Deadlock-Free Task Exchange. Drones, 6.
  • Zhou, X., Qin, T., and Meng, L. (2022). Maneuvering Spacecraft Orbit Determination Using Polynomial Representation. Aerospace, 9.
  • Li, W., Lyu, Y., Dai, S., Chen, H., Shi, J., and Li, Y. (2022). A Multi-Target Consensus-Based Auction Algorithm for Distributed Target Assignment in Cooperative Beyond-Visual-Range Air Combat. Aerospace, 9.
  • Abdar, (2021), Inf. Fusion, 76, pp. 243, 10.1016/j.inffus.2021.05.008
  • Zhang, (2021), IEEE Trans. Ind. Inform., 17, pp. 8475, 10.1109/TII.2021.3064351
  • Du, (2020), IEEE Trans. Control Netw. Syst., 7, pp. 1330, 10.1109/TCNS.2020.2975228
  • Han, J., Wu, J., Zhang, L., Wang, H., Zhu, Q., Zhang, C., Zhao, H., and Zhang, S. (2022). A Classifying-Inversion Method of Offshore Atmospheric Duct Parameters Using AIS Data Based on Artificial Intelligence. Remote Sens., 14.
  • Silver, (2016), Nature, 529, pp. 484, 10.1038/nature16961
  • Brown, (2018), Science, 359, pp. 418, 10.1126/science.aao1733
  • Li, (2022), Def. Technol., 18, pp. 1006, 10.1016/j.dt.2021.04.009
  • Kang, Y., Pu, Z., Liu, Z., Li, G., Niu, R., and Yi, J. (2022). Advances in Guidance, Navigation and Control, Springer.
  • Li, (2022), Chin. J. Electron., 31, pp. 107
  • Li, (2022), Def. Technol., 18, pp. 368, 10.1016/j.dt.2021.01.005
  • Zhang, (2021), Aerosp. Sci. Technol., 115, pp. 106815, 10.1016/j.ast.2021.106815
  • Li, (2022), Inf. Sci., 594, pp. 305, 10.1016/j.ins.2022.02.025
  • Ruan, (2022), IEEE/CAA J. Autom. Sin., 9, pp. 1
  • Hu, J., Wang, L., Hu, T., Guo, C., and Wang, Y. (2022). Autonomous Maneuver Decision Making of Dual-UAV Cooperative Air Combat Based on Deep Reinforcement Learning. Electronics, 11.
  • Du, (2021), IEEE Trans. Intell. Transp. Syst., 23, pp. 5859, 10.1109/TITS.2021.3060377
  • Austin, (1990), J. Guid. Control Dyn., 13, pp. 1143, 10.2514/3.20590
  • Yang, (2019), IEEE Access, 8, pp. 363, 10.1109/ACCESS.2019.2961426
  • Zhang, (2020), Acta Armamentarii, 41, pp. 1613
  • Li, (2022), Def. Technol., 18, pp. 1697, 10.1016/j.dt.2021.09.014
  • Hu, (2022), Eng. Appl. Artif. Intell., 111, pp. 104767, 10.1016/j.engappai.2022.104767
  • Du, (2021), IEEE Trans. Aerosp. Electron. Syst., 57, pp. 3084, 10.1109/TAES.2021.3082672
  • Dong, (2002), J. Syst. Simul., 14, pp. 723
  • Jiang, (2008), Fire Control Command Control, 33, pp. 8
  • Shen, (2014), Comput. Simul., 31, pp. 60
  • Cruz, (2002), IEEE Trans. Aerosp. Electron. Syst., 38, pp. 989, 10.1109/TAES.2002.1039415
  • Maschler, M., Solan, E., and Zamir, S. (2013). Game Theory, Cambridge University Press.
  • Parthasarathy, T., and Raghavan, T.E.S. (1917). Some Topics in Two-Person Games, American Elsevier Publishing Company.
  • Huang, (2018), J. Syst. Eng. Electron., 29, pp. 86, 10.21629/JSEE.2018.01.09