Advertisement

Multimedia Tools and Applications

, Volume 78, Issue 1, pp 415–436 | Cite as

Multi-model cooperative task assignment and path planning of multiple UCAV formation

  • Hanqiao HuangEmail author
  • Tao ZhuoEmail author
Article

Abstract

Multi-model techniques have shown an outstanding effectiveness in the cooperative task assignment and path planning of the unmanned combat aerial vehicle(UCAV) formation. With cooperative decision making and control, the cooperative combat of the UCAV formation are described and the mathematical model of the UCAV formation is built. Then, the task assignment model of the UCAV formation is developed according to flight characteristics of the UCAV formation and constraints in battlefield. The cooperative task assignment problem is solved using the improved particle swarm optimization(IPSO), ant colony algorithm(ACA) and genetic algorithm(GA) respectively. The comparative analysis is conducted in the aspects of the precision and the search speed. The path planning model of the UCAV formation is constructed considering the oil cost, threat cost, crash cost and time cost. The cooperative path planning problem is solved based on the evolution algorithm(EA), in which unique coding scheme of chromosomes is designed, and the crossover operator and mutation operator are redefined. Simulation results demonstrate that the UCAV formation can choose the best algorithm according to the real battlefield environment, which can solve the cooperative task assignment and path planning problems quickly and effectively to meet the demand of the cooperative combat.

Keywords

Formation UCAV Task assignment Path planning Multi-model Particle Swarm Optimization (PSO) 

Notes

Acknowledgments

The work was supported by National Natural Science Foundations of China (No.61601505), the Natural Science Foundation of Shaanxi Province(No.2016JQ6050), the Aviation Science Foundations of China (No.20155196022) and the National Research Foundation, Prime Ministers Office, Singapore under its International Research Centre in Singapore Funding Initiative.

References

  1. 1.
    (2014). Single/cross-camera multiple-person tracking by graph matching. Neurocomputing 139, 220–232Google Scholar
  2. 2.
    Aibinu AM, Salau HB, Rahman NA, Nwohu MN, Akachukwu CM (2016) A novel clustering based genetic algorithm for route optimization. Engineering Science & Technology An International JournalGoogle Scholar
  3. 3.
    Anitha G, Kumar RNG (2012) Vision based autonomous landing of an unmanned aerial vehicle. Procedia Eng 38:2250–2256CrossRefGoogle Scholar
  4. 4.
    Cao L, Shun Tan H, Peng H, Cong Pan M (2014) Multiple uavs hierarchical dynamic task allocation based on pso-fsa and decentralized auction. In: Robotics and biomimetics (ROBIO), 2014 IEEE international conference on. IEEE, pp 2368–2373Google Scholar
  5. 5.
    Edison E, Shima T (2011) Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms. Comput Oper Res 38(1):340–356MathSciNetCrossRefGoogle Scholar
  6. 6.
    Evers L, Barros AI, Monsuur H, Wagelmans A (2014) Online stochastic uav mission planning with time windows and time-sensitive targets. Eur J Oper Res 238(1):348–362CrossRefGoogle Scholar
  7. 7.
    Evers L, Dollevoet T, Barros AI, Monsuur H (2014) Robust uav mission planning. Ann Oper Res 222(1):293–315MathSciNetCrossRefGoogle Scholar
  8. 8.
    Francis MS (1971) Unmanned air systems: Challenge and opportunity. J Aircr 49(6):1652–1665CrossRefGoogle Scholar
  9. 9.
    Guo J, Wang Z, Zheng M, Wang Y (2014) Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm. Chin J Aeronaut 27(6):1477–1487CrossRefGoogle Scholar
  10. 10.
    Halman N (2016) A deterministic fully polynomial time approximation scheme for counting integer knapsack solutions made easy. Theor Comput Sci 645:41–47MathSciNetCrossRefGoogle Scholar
  11. 11.
    Huang H, Zhou H, Cai Y (2015) Study on multi-path planning and tracking control of the ucav based on evolutionary algorithm pp 1762–1766Google Scholar
  12. 12.
    Huang H, Zhu D, Ding F (2014) Dynamic task assignment and path planning for multi-auv system in variable ocean current environment. J Intell Robot Syst 74(3):999–1012CrossRefGoogle Scholar
  13. 13.
    Ide J, Kobis E (2014) Concepts of efficiency for uncertain multi-objective optimization problems based on set order relations. Math Meth Oper Res 80(1):99–127MathSciNetCrossRefGoogle Scholar
  14. 14.
    Jia Y, Chen W, Gu T et al (2017) A dynamic logistic dispatching system with set-based particle swarm optimization. IEEE Trans Syst Man Cybern: Syst pp 1–15Google Scholar
  15. 15.
    Li MD, Zhao H, Weng XW, Han T (2016) A novel nature-inspired algorithm for optimization: Virus colony search. Adv Eng Softw 92(C):65–88CrossRefGoogle Scholar
  16. 16.
    Liu H, Zhang P, Hu B, Moore P (2015) A novel approach to task assignment in a cooperative multi-agent design system. Appl Intell 43(1):162–175CrossRefGoogle Scholar
  17. 17.
    Mendon D, Mathias AR, Nedjah N, Luiza DM (2016) Efficient distributed algorithm of dynamic task assignment for swarm robotics. Neurocomputing 172(C):345–355CrossRefGoogle Scholar
  18. 18.
    Narasimha K, Kivelevitch E, Sharma B, Kumar M (2013) An ant colony optimization technique for solving min-max multi-depot vehicle routing problem. Swarm Evol Comput 13:63–73CrossRefGoogle Scholar
  19. 19.
    Nie W, Liu A, Li W, Su Y (2016) Cross-view action recognition by cross-domain learning. Image Vis Comput 55:109–118CrossRefGoogle Scholar
  20. 20.
    Noei S, Sargolzaei A, Abbaspour A, Kang Y (2016) A decision support system for improving resiliency of cooperative adaptive cruise control systems. Procedia Comput Sci 95:489–496CrossRefGoogle Scholar
  21. 21.
    Oh G, Kim Y, Ahn J, Choi HL (2016) Pso-based optimal task allocation for cooperative timing missions. IFAC-PapersOnLine 49(17):314–319CrossRefGoogle Scholar
  22. 22.
    Oh G, Kim Y, Ahn J, Choi HL (2017) Market-based task assignment for cooperative timing missions in dynamic environments. J Intell Robot Syst pp 1–27Google Scholar
  23. 23.
    Sarasola B, Doerner KF, Schmid V, Alba E (2016) Variable neighborhood search for the stochastic and dynamic vehicle routing problem. Ann Oper Res 236(2):425–461MathSciNetCrossRefGoogle Scholar
  24. 24.
    Song B, Kim J, Morrison JR (2016) Rolling horizon path planning of an autonomous system of uavs for persistent cooperative service: Milp formulation and efficient heuristics. J Intell Robot Syst 84:241–258CrossRefGoogle Scholar
  25. 25.
    Song Q, Zhang H (2010) Research on multi-lateral multi-issue negotiation based on hybrid genetic algorithm in e-commerce pp 706–709Google Scholar
  26. 26.
    Wang M, Wan Y, Ye Z, Lai X (2017) Remote sensing image classification based on the optimal support vector machine and modified binary coded ant colony optimization algorithm. Inf Sci pp 50–68Google Scholar
  27. 27.
    Williams P (2012) Aircraft trajectory planning for terrain following incorporating actuator constraints. J Aircr 42(5):1358–1361CrossRefGoogle Scholar
  28. 28.
    Wu Y, Qu XJ (2013) Path planning for taxi of carrier aircraft launching. Sci China Technol Sci 56(6):1561–1570CrossRefGoogle Scholar
  29. 29.
    Yu X, Zhang Y (2015) Sense and avoid technologies with applications to unmanned aircraft systems: Review and prospects. Prog Aerosp Sci 74:152–166CrossRefGoogle Scholar
  30. 30.
    Zhang H, Shang X, Luan H, Wang M, Chua TS (2016) Learning from collective intelligence: Feature learning using social images and tags. ACM Trans Multimed Comput Commun Appl (TOMM) 13Google Scholar
  31. 31.
    Zhang H, Shen F, Liu W, He X, Luan H, Chua TS (2016) Discrete collaborative filtering. In: Proc. of SIGIR, vol 16Google Scholar
  32. 32.
    Zhang P, Zhuo T, Huang W, Chen K, Kankanhalli M (2017) Online object tracking based on cnn with spatial-temporal saliency guided sampling. NeurocomputingGoogle Scholar
  33. 33.
    Zhang P, Zhuo T, Xie L, Zhang Y (2016) Deformable object tracking with spatiotemporal segmentation in big vision surveillance. Neurocomputing 204:87–96CrossRefGoogle Scholar
  34. 34.
    Zhang P, Zhuo T, Zhang Y, Tao D, Cheng J (2016) Online tracking based on efficient transductive learning with sample matching costs. Neurocomputing 175:166–176CrossRefGoogle Scholar
  35. 35.
    Zhang P, Zhuo T, Zhang Y, Xie L, Tao D (2016) Real-time tracking-by-learning with high-order regularization fusion for big video abstraction. Signal Process 124:246–258CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Northwestern Polytechnical UniversityXi’anPeople’s Republic of China
  2. 2.Sensor-enhanced Social Media (SeSaMe) CentreNational University of SingaporeSingaporeSingapore

Personalised recommendations