Unmanned aerial vehicle task distribution method based on quantum pigeon flock mechanism

Abstract

The invention discloses an unmanned aerial vehicle (UAV) task distribution method based on a quantum pigeon flock mechanism, belonging to the field of UAV resource distribution. The method comprises the following steps: establishing a phased task assignment model of an unmanned aerial vehicle; determining the tasks to be performed and initializing the quantum pigeon flocks; calculating the fitnessvalue of each quantum pigeon, and selecting the local optimal position and the global optimal position; updating the quantum rotation angle vector to update the quantum velocity of each quantum pigeon to obtain the position of the quantum pigeon; evaluating the fitness of each quantum pigeon; determining a local optimal position and a global optimal position; judging whether the maximum number ofiterations is reached; outputting the global optimal position; judging whether the assignment is completed or not; obtaining a task distribution scheme. The method realizes higher convergence precision, faster convergence speed and more reasonable task allocation scheme with less time cost, can effectively solve the requirement of multiple constraints on the unmanned aerial vehicle, and can obtain more reasonable task allocation scheme of the unmanned aerial vehicle.

Description

technical field [0001] The invention belongs to the field of unmanned aerial vehicle resource allocation, and in particular relates to an unmanned aerial vehicle task allocation method based on a quantum pigeon group mechanism. Background technique [0002] Drones generally refer to unmanned aircraft that can be controlled autonomously or remotely by ground operators. Compared with manned aircraft, it has the advantages of small size, low cost, easy to use, low requirements for the combat environment, and strong battlefield survivability. Therefore, it can be used to replace human pilots to perform dangerous, stressful, and repetitive tasks. In the military field, it can be used to complete battlefield reconnaissance and surveillance, positioning and calibration, as a target for artillery and missiles, etc.; in the civilian field, it can be used for map surveying, geological exploration, communication relay, etc. Therefore, whether in the military or in the civilian field, ...

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Problems solved by technology

"UCAV collaborative multi-objective allocation based on multi-group improved firefly algorithm" published in "Journal of Northwestern Polytechnical University" (2014, Vol.32, No.3, pp.451-456) will improve the firefly algorithm (MIGSO) Applied to the task assignment problem of drones, although the improved firefly algorithm has a faster convergence speed, it is easy to fall into a local optimal solution

In "Acta Aeronautics Sinica" (2010, Vol.31, No.3, pp.626-631), Li Yan et al. published "Cooperative Air Combat Firepower Allocation Based on SA-DPSO Hybrid Optimization Algorithm" by combining simulated annealing and discrete particle swarm optimization. The optimization algorithm is applied to the UAV task assignment problem. Although the characteristics of the two algorithms are combined, the optimization performance for high-dimensional nonlinear problems is still not high.

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