Multiple-unmanned-aerial-vehicle cooperated multi-target distribution method

Abstract

The invention relates to a multiple-unmanned-aerial-vehicle cooperated multi-target distribution method which comprises the steps of firstly numbering a plurality of unmanned aerial vehicles and a plurality of targets, wherein U represents the number of the unmanned aerial vehicles and T represents the number of the targets; then constructing a flight cost model according to magnitudes of U and T and an unmanned aerial vehicle flight cost parameter, wherein the unmanned aerial vehicle flight cost parameter comprises a flight range cost, an execution time cost and a damage cost; and finally performing optimized solving on the flight cost model by means of a heuristic genetic algorithm until a requirement for an optimized target is satisfied. According to the multiple-unmanned-aerial-vehicle cooperated multi-target distribution method, through adding the unmanned aerial vehicle damage cost in the flight cost, three most basic conditions are extracted for modeling for aiming at a relationship between the unmanned aerial vehicles and the number of targets so that a model approaches to reality; and in target distribution, the heuristic genetic algorithm is utilized. Through introducing heuristic information, algorithm execution efficiency is effectively increased and a precocity problem of the genetic algorithm is prevented. The multiple-unmanned-aerial-vehicle cooperated multi-target distribution method is better than a basic genetic algorithm and a differential evolution algorithm at aspects of convergence speed and convergence value.

Description

technical field [0001] The invention belongs to the technical field of multi-UAV collaborative control, and particularly relates to a multi-UAV cooperative multi-target allocation method, which adopts a heuristic genetic algorithm when performing target allocation, and has faster convergence speed by introducing heuristic information. Avoid the premature problem of genetic algorithm. Background technique [0002] With its simplicity and flexibility, drones play an important role in replacing humans to complete dangerous, boring and harsh tasks. The complex flight environment and diverse mission requirements make multi-UAV collaboration a trend. Multi-UAV collaborative multi-target assignment is to assign specific tasks to UAVs based on the overall task requirements, comprehensively considering flight, environment and task constraints, so as to improve the efficiency of task completion. The multi-UAV cooperative multi-objective allocation problem is a combinatorial optimiza...

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Problems solved by technology

[0004] In order to overcome the deficiencies in the prior art, the present invention provides a multi-UAV cooperative multi-objective allocation method, which is aimed at the problem of incomplete consideration in the modeling of multi-UAV cooperative multi-objective allocation problems, and relatively simple considerations in the construction of objective functions. The UAV damage cost is added to the flight cost; for the relationship between the UAV and the number of targets, three basic situations are extracted for modeling to make the model close to reality; when the target is assigned, a heuristic genetic algorithm is used to Heuristic information can effectively speed up the execution efficiency of the algorithm and avoid the premature problem of the genetic algorithm

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