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

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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.

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References

  1. (2014). Single/cross-camera multiple-person tracking by graph matching. Neurocomputing 139, 220–232

  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 Journal

  3. Anitha G, Kumar RNG (2012) Vision based autonomous landing of an unmanned aerial vehicle. Procedia Eng 38:2250–2256

    Article  Google Scholar 

  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 2368–2373

  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):340–356

    Article  MathSciNet  Google Scholar 

  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):348–362

    Article  Google Scholar 

  7. Evers L, Dollevoet T, Barros AI, Monsuur H (2014) Robust uav mission planning. Ann Oper Res 222(1):293–315

    Article  MathSciNet  Google Scholar 

  8. Francis MS (1971) Unmanned air systems: Challenge and opportunity. J Aircr 49(6):1652–1665

    Article  Google Scholar 

  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):1477–1487

    Article  Google Scholar 

  10. Halman N (2016) A deterministic fully polynomial time approximation scheme for counting integer knapsack solutions made easy. Theor Comput Sci 645:41–47

    Article  MathSciNet  Google Scholar 

  11. Huang H, Zhou H, Cai Y (2015) Study on multi-path planning and tracking control of the ucav based on evolutionary algorithm pp 1762–1766

  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):999–1012

    Article  Google Scholar 

  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):99–127

    Article  MathSciNet  Google Scholar 

  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 1–15

  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):65–88

    Article  Google Scholar 

  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):162–175

    Article  Google Scholar 

  17. Mendon D, Mathias AR, Nedjah N, Luiza DM (2016) Efficient distributed algorithm of dynamic task assignment for swarm robotics. Neurocomputing 172(C):345–355

    Article  Google Scholar 

  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 13:63–73

    Article  Google Scholar 

  19. Nie W, Liu A, Li W, Su Y (2016) Cross-view action recognition by cross-domain learning. Image Vis Comput 55:109–118

    Article  Google Scholar 

  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 95:489–496

    Article  Google Scholar 

  21. Oh G, Kim Y, Ahn J, Choi HL (2016) Pso-based optimal task allocation for cooperative timing missions. IFAC-PapersOnLine 49(17):314–319

    Article  Google Scholar 

  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 1–27

  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):425–461

    Article  MathSciNet  Google Scholar 

  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 84:241–258

    Article  Google Scholar 

  25. Song Q, Zhang H (2010) Research on multi-lateral multi-issue negotiation based on hybrid genetic algorithm in e-commerce pp 706–709

  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 50–68

  27. Williams P (2012) Aircraft trajectory planning for terrain following incorporating actuator constraints. J Aircr 42(5):1358–1361

    Article  Google Scholar 

  28. Wu Y, Qu XJ (2013) Path planning for taxi of carrier aircraft launching. Sci China Technol Sci 56(6):1561–1570

    Article  Google Scholar 

  29. Yu X, Zhang Y (2015) Sense and avoid technologies with applications to unmanned aircraft systems: Review and prospects. Prog Aerosp Sci 74:152–166

    Article  Google Scholar 

  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) 13

  31. Zhang H, Shen F, Liu W, He X, Luan H, Chua TS (2016) Discrete collaborative filtering. In: Proc. of SIGIR, vol 16

  32. Zhang P, Zhuo T, Huang W, Chen K, Kankanhalli M (2017) Online object tracking based on cnn with spatial-temporal saliency guided sampling. Neurocomputing

  33. Zhang P, Zhuo T, Xie L, Zhang Y (2016) Deformable object tracking with spatiotemporal segmentation in big vision surveillance. Neurocomputing 204:87–96

    Article  Google Scholar 

  34. Zhang P, Zhuo T, Zhang Y, Tao D, Cheng J (2016) Online tracking based on efficient transductive learning with sample matching costs. Neurocomputing 175:166–176

    Article  Google Scholar 

  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 124:246–258

    Article  Google Scholar 

Download references

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.

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Correspondence to Hanqiao Huang or Tao Zhuo.

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Huang, H., Zhuo, T. Multi-model cooperative task assignment and path planning of multiple UCAV formation. Multimed Tools Appl 78, 415–436 (2019). https://doi.org/10.1007/s11042-017-4956-7

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  • DOI: https://doi.org/10.1007/s11042-017-4956-7

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