Advertisement

Cluster Computing

, Volume 22, Supplement 3, pp 5175–5184 | Cite as

Multi-base multi-UAV cooperative reconnaissance path planning with genetic algorithm

  • Yan Cao
  • Wanyu WeiEmail author
  • Yu Bai
  • Hu Qiao
Article
  • 325 Downloads

Abstract

Describing cooperative reconnaissance is crucial for air traffic relating to multiple unmanned aerial vehicles (UAVs) loaded in different bases in an increasingly complex battlefield environment. Compared with the traditional problem that all UAVs took off from just one base, this paper is to address reconnaissance missions, which must be done in partnership among multiple UAVs in different bases. To improve missions’ reliability, residence time in effective detection of enemy radars should be mitigated under the premise of missions completed by UAVs. This paper transforms the minimum residence time into the shortest path combinatorial optimization, and discretizes heading angles. Graph theory is applied to analyze path problems and a global model with numerous constraint conditions can be built. Finally, a valuable reconnaissance path planning can be generated through solving the model with genetic algorithm. Also an application example that eight UAVs in four bases finish reconnaissance missions involving sixty-eight targets is established, and then an optimal solution is got to explain both the feasibility and efficiency of the proposed modularization and algorithm.

Keywords

Combinatorial optimization UAV Multi-base Genetic algorithm Path planning 

Notes

Acknowledgements

The paper was supported by Key Problem Tackling Project of Shaanxi Scientific and Technological Office (2016GY-024), and National Natural Science Foundation of China (Grant No. 51705392), Xi’an Technological University President Foundation (Grant No. XAGDXJJ16004).

References

  1. 1.
    ENex, F., Remondino, F.: UAV for 3D mapping applications: a review. Appl. Geomat. 6(1), 1–15 (2014)CrossRefGoogle Scholar
  2. 2.
    Chen, Y., Luo, G., Mei, Y., et al.: UAV path planning using artificial potential field method updated by optimal control theory. Int. J. Syst. Sci. 47(6), 1407–1420 (2016)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Ollero, Aníbal, et al.: Multiple Heterogeneous Unmanned Aerial Vehicles. Springer, Berlin Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Valavanis, K.P.: Advances in Unmanned Aerial Vehicles: State of the Art and the Road to Autonomy. Springer, New York (2007)CrossRefGoogle Scholar
  5. 5.
    Austin, R.: Unmanned aircraft systems: UAVS design, development and deployment. J. Publ. Chestnet. Org 79(50), 31–36 (2010)Google Scholar
  6. 6.
    Ingersoll, B.T., Ingersoll, J.K., DeFranco, P., et al.: UAV path-planning using Bézier curves and a receding horizon approach. In: AIAA Modeling and Simulation Technologies Conference, p. 3675 (2016)Google Scholar
  7. 7.
    Li, B., Chiong, R., Lin, M.: A two-layer optimization framework for UAV path planning with interval uncertainties. Computational Intelligence in Production and Logistics Systems (CIPLS), 2014 IEEE Symposium on. IEEE, pp. 120–127 (2014)Google Scholar
  8. 8.
    Zhang, J., Li, Q., Cheng, N., et al.: Non-linear flight control for unmanned aerial vehicles using adaptive backstepping based on invariant manifolds. Proc. Inst. Mech. Eng. Part G. J. Aerosp. Eng. 227(1), 33–44 (2013)CrossRefGoogle Scholar
  9. 9.
    Roberge, V., Tarbouchi, M., Labonté, G.: Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans. Ind. Inform. 9(1), 132–141 (2013)CrossRefGoogle Scholar
  10. 10.
    Yao, J., Lin, C., Xie, X., et al.: Path planning for virtual human motion using improved A* star algorithm. IEEE Information Technology: New Generations (ITNG), 2010 Seventh International Conference. IEEE press, pp. 1154–1158 (2010)Google Scholar
  11. 11.
    Lin, L., Goodrich, M.A.: Sliding autonomy for UAV path-planning: adding new dimensions to autonomy management. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems. International Foundation for Autonomous Agents and Multiagent Systems, pp. 1615–1624 (2010)Google Scholar
  12. 12.
    Li, S.J., XIAO, Q.G., GAO, Y.H., et al.: UAV route planning dynamic estimation method for multi-constraints. Command Control. Simul. 34(2), 36–39 (2012). [Chinese]Google Scholar
  13. 13.
    Deng, Q., Yu, J., Wang, N.: Cooperative task assignment of multiple heterogeneous unmanned aerial vehicles using a modified genetic algorithm with multi-type genes. Chin. J. Aeronaut. 26(5), 1238–1250 (2013)CrossRefGoogle Scholar
  14. 14.
    Shamma, J.S.: Cooperative Control of Distributed Multi-Agent Systems. Wiley, Chichester (2008)Google Scholar
  15. 15.
    Rasmussen, S., Shima, T., Shima, T., et al.: UAV Cooperative Decision and Control. Society for Industrial and Applied Mathematics, Canada (2009)zbMATHGoogle Scholar
  16. 16.
    Banda, S., Doyle, J., Murray, R., et al.: Research needs in dynamics and control for uninhabited aerial vehicles. http://www.cds.caltech.edu/murray/notes/uavnov97.html, Panel Report Nov (1997)
  17. 17.
    Murphey, R., Pardalos, P.M.: Cooperative Control and Optimization, pp. 539–551. Kluwer Academic Publishers, Boston (2002)CrossRefGoogle Scholar
  18. 18.
    Silva Arantes, J., Silva Arantes, M., Motta Toledo, C.F., et al.: Heuristic and genetic algorithm approaches for UAV path planning under critical situation. Int. J. Artif. Intell. Tools 26(01), 1760008 (2017)CrossRefGoogle Scholar
  19. 19.
    Duan, H., Luo, Q., Shi, Y., et al.: Hybrid particle swarm optimization and genetic algorithm for multi-UAV formation reconfiguration. IEEE Comput. Intell. Mag. 8(3), 16–27 (2013)CrossRefGoogle Scholar
  20. 20.
    Ma, Y.H., Jing, Z., Zhou, D.Y.: A faster pruning optimization algorithm for task assignment. J. Northwest. Polytech. Univ. 31(1), 40–43 (2013). [Chinese]Google Scholar
  21. 21.
    Li, J., Fu, X.W., GAO, X.G.: Cooperative multi-UAV path planning with communication constraints. Electron. Opt. Control. 20(6), 29–33 (2013). [Chinese]Google Scholar
  22. 22.
    Wu, Q.P., ZHOU, S.L., LIU, W., et al.: Multi-UAV cooperative search strategy for diverse types of targets. Electron. Opt. Control. 4, 28–32 (2016). [Chinese]Google Scholar
  23. 23.
    Di, B., Zhou, R., Ding, Q.X.: Distributed coordinated heterogeneous task allocation for unmanned aerial vehicles. Control Decis. 28(2), 274–278 (2013)Google Scholar
  24. 24.
    Xu, S., Dogançay, K., Hmam, H.: Distributed path optimization of multiple UAVs for AOA target localization. Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on IEEE, pp. 3141–3145 (2016)Google Scholar
  25. 25.
    Grancharova, A., Grøtli, E.I., Ho, D.T., et al.: UAVs trajectory planning by distributed MPC under radio communication path loss constraints. J. Intell. Robot. Syst. 79(1), 115 (2015)CrossRefGoogle Scholar
  26. 26.
    Edison, E., Shima, T.: Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms. Comput. Oper. Res. 38(1), 340–356 (2011)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Avellar, G.S.C., Pereira, G.A.S., Pimenta, L.C.A., et al.: Multi-uav routing for area coverage and remote sensing with minimum time. Sensors 15(11), 27783–27803 (2015)CrossRefGoogle Scholar
  28. 28.
    Liu, Y., Yu, Y.: Encoding theory and application of genetic algorithm. Comput. Eng. Appl. 3, 86–89 (2006)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Mechatronic EngineeringXi’an Technological UniversityXi’anChina

Personalised recommendations