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


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.


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



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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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

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