Multi-unmanned aerial vehicle cooperative task allocation method based on improved genetic algorithm

Abstract

The invention provides a multi-unmanned aerial vehicle cooperative task allocation method based on an improved genetic algorithm. The method comprises the following three steps: establishing constraint equations such as the minimum turning radius of an unmanned aerial vehicle and the number of tasks required by a target, and a multi-unmanned aerial vehicle cooperative task allocation model based on Dubins flight path cost; generating an initial population of a predetermined scale conforming to model constraint conditions; taking the Dubins track path cost of the unmanned aerial vehicle as a fitness function, and iteratively updating the initial population by using genetic operations such as elite strategy, selection, crossover, variation and the like of an improved genetic algorithm to generate a feasible solution which minimizes the target function in fixed iteration times, and taking the feasible solution as a result of multi-unmanned aerial vehicle cooperative task allocation and route planning. The method has wide application value in multi-unmanned aerial vehicle cooperative task combat, is beneficial to implementation of multi-unmanned aerial vehicle multi-target cooperativetask execution, and improves the task completion efficiency. The method has important significance in the field of multi-unmanned aerial vehicle cooperative control.

Description

technical field [0001] The invention belongs to the technical field of UAV control and decision-making, and in particular relates to a multi-UAV cooperative task assignment method based on an improved genetic algorithm. Background technique [0002] With the increasing complexity of the electromagnetic environment in the modern battlefield and the increasing integration of attack weapons, the mission execution of man-machines has been threatened unprecedentedly. Compared with manned aircraft, drones have the advantage of low casualty rate or even zero casualty rate in the dangerous environment of the modern battlefield. Since there is no need to consider airborne life, the maneuverability, low detectability and continuous combat capability of UAVs have been improved, and the R&D, manufacturing, use and maintenance costs of UAVs are low, so UAVs meet the needs of the times. It can perform boring, harsh, dangerous, and covert tasks in complex battlefields. However, even if t...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to propose a multi-unmanned aerial vehicle cooperative task assignment method based on an improved genetic algorithm, under the condition of considering the Dubins track path cost of the unmanned aerial vehicle to perform tasks, to solve the effective problem of heterogeneous unmanned aerial vehicles to perform multi-target tasks Resource allocation to enhance the collaborative work of multiple UAVs is conducive to improving the efficiency of task completion and meeting the needs of modern battlefields

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