UAV group task allocation method based on quantum crow group search mechanism

Abstract

The invention relates to a UAV group task allocation method based on a quantum crow group search mechanism. The UAV group task allocation method comprises the steps of: establishing a UAV group task allocation model from a plurality of start points to a plurality of tasks, wherein the UAV group task allocation model comprises UAV model numbers, start and end points, and an allocation model; initializing a quantum crow group; calculating the fitness of each quantum crow according to a fitness function, and storing a position of the quantum crow corresponding to the minimum value of the fitnessfunction as a globally optimal food position; updating a quantum position and the position of each quantum crow; and calculating the fitness of each quantum crow according to a fitness function, determining a hidden food position of each quantum crow, finding the optimal food position so far, outputting the globally optimal food position if the maximum iteration algebra is reached, and mapping themaximum iteration algebra into a task allocation matrix. The UAV group task allocation method solves the discrete multi-constrained objective function solving problem, designs a discrete quantum crowalgorithm as an evolution strategy, and has the advantages of fast convergence speed and high convergence precision.

Description

technical field [0001] The invention relates to a task allocation method for unmanned aerial vehicles, in particular to a task allocation method for unmanned aerial vehicles based on a quantum crow swarm search mechanism, which belongs to the field of autonomous control of unmanned aerial vehicles. Background technique [0002] UAV is also called unmanned aerial vehicle (Unmanned Aerial Vehicle, UAV). During its use, it does not need to carry an operator, provides lift with aerodynamic force, and can fly autonomously through remote control or under the control of a predetermined program. Fly, and perform specific tasks by carrying mission equipment. UAVs have the advantages of small size, flexible use, good concealment, and strong adaptability. They can complete some specific tasks and tasks that humans cannot reach and engage in in various harsh, dangerous, and extreme environments. The development, production and use costs of UAVs are much lower than those of manned aircr...

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Problems solved by technology

[0005] (2011, Vol.18, No.10, pp.28–31) published a task allocation model in "Multi-UCAV Ground Attack Target Allocation Based on Game Theory", using game theory algorithms to seek optimal task allocation, However, the algorithm model is complex, the accuracy is not high enough and the amount of calculation is large

In "Control and Decision Making" (2010, Vol.25, No.9, pp.1359-1364), "Multi-UAV Task Allocation Method Based on Particle Swarm Algorithm" was published by Li Wei et al. The task allocation problem of UAV, but the particle swarm algorithm is easy to fall into local optimum, and the convergence accuracy needs to be improved

In the "Acta Aerospace Sciences" (2014, Vol.25, No.9, pp.1626-631) proposed by Li Yan et al. "Cooperative air combat firepower allocation based on SA-DPSO hybrid optimization algorithm" combines simulated annealing algorithm and particle swarm optimization algorithm Combined with UAV task assignment, this method has a better convergence speed, but it is easy to fall into the curse of dimensionality, and the optimization performance is not enough

[0006] Because the above UAV task assignment methods are nonlinear solution methods, it is very easy to fall into local extremum during the solution process, and it is difficult to obtain the global optimal solution

However, the existing UAV task allocation methods seldom consider various evaluation indicators and constraints in the task allocation of UAV groups, so their application range is limited.

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