Multiple-unmanned-aerial-vehicle cooperated multi-target distribution method
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A distribution method and multi-UAV technology, applied in mechanical equipment, genetic laws, combustion engines, etc., can solve the problems of single construction objective function, damage, incomplete modeling consideration, etc., to ensure population diversity and performance improvement. , the effect of avoiding precocious problems
Active Publication Date: 2017-03-22
THE PLA INFORMATION ENG UNIV
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[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
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Embodiment 1
[0048] Embodiment one, see figure 1 As shown, a multi-UAV cooperative multi-target allocation method includes the following steps:
[0049] Step 1. Number multiple drones and multiple targets respectively, use 1, 2, ..., U to represent the drone number, 1, 2, ..., T to represent the target number, where U represents no one The number of machines, T represents the target number;
[0050] Step 2. Construct the flight cost model F(x) according to the size relationship between U and T and the flight cost parameters of the UAV, where the size relationship between U and T includes: U>T, U=T and U
[0051] Step 3. Use the heuristic genetic algorithm to optimize and solve the flight cost model until the optimization objective is met.
[0052] In the process of modeling, the present invention analyzes the quantitative relationship between the drone and the target, and consider...
Embodiment 2
[0053] Embodiment two, see Figure 1-11 As shown, a multi-UAV cooperative multi-target allocation method includes the following content:
[0054] First, number multiple drones and multiple targets respectively, use 1, 2, ..., U to represent the drone number, 1, 2, ..., T to represent the target number, where U represents the drone Quantity, T represents the target quantity. Multiple UAVs are distributed before take-off, and it is assumed that the positions of all UAVs and all targets are known before target allocation. Each drone must be assigned to a target, and each target must have a drone corresponding to it.
[0055] Then, according to the size relationship between U and T and the UAV flight cost parameters, the flight cost model F(x) is constructed, where the size relationship between U and T includes three situations: U>T, U=T and U
[0056] The voyage cost is expressed as: Amon...
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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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