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Intelligent optimization method and system for cooperative task allocation of unmanned aerial vehicle and vehicle

A task allocation and intelligent optimization technology, applied in the field of drones, can solve problems such as inability to solve effectively, and achieve the effect of saving the time for judging infeasible chromosomes and correcting them

Pending Publication Date: 2021-12-17
HEFEI UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Aiming at the deficiencies of the prior art, the present invention provides an intelligent optimization method and system for UAV-vehicle collaborative task assignment, which solves the problem of large-scale tasks after building a UAV-vehicle collaborative task assignment model. , the problem that cannot be effectively solved

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  • Intelligent optimization method and system for cooperative task allocation of unmanned aerial vehicle and vehicle
  • Intelligent optimization method and system for cooperative task allocation of unmanned aerial vehicle and vehicle
  • Intelligent optimization method and system for cooperative task allocation of unmanned aerial vehicle and vehicle

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Embodiment 1

[0135] like figure 1 As shown, the present invention provides an intelligent optimization method of a co-assignment of a drone and a vehicle, the method comprising:

[0136] S1, get the unmanned and vehicle collaborative task allocation model, genetic algorithm preset parameter collection;

[0137] S2, using the car machine synergistic mixed encoding method to form a chromosome, and construct initial group;

[0138] S3, put the current group as a parent population, based on the drone and vehicle synergistic task allocation model uses the car machine synergistic mixed encoding method to calculate the adaptivity value of each chromosome in the population, and then use preset selection, cross-and variation operation Operate the chromosome in the current population;

[0139] S4, using the car machine synergistic discrimination method to discriminate the feasibility of the chromosome in the current population; if it is discriminated, enter S6; otherwise, enter S5;

[0140] S5, using a ...

Embodiment 2

[0237] The present invention also provides an intelligent optimization system assigned by a procedure and vehicle synergistic task, the system comprising:

[0238] Data acquisition module for obtaining a collection of preset parameters of drone and vehicle collaborative task assignment model and genetic algorithm;

[0239] The initial population generation and adaptivity value calculation module is used to generate chromosomes using a car machine synergistic mixed encoding method, and construct initiation group; and calculate each of the population based on the drone and vehicle synergistic task distribution model. Adaptivity value of chromosome;

[0240] Chromatography Module for uses the current population as a parent population, and then operates chromosome in the current population using preset selection, cross-and variation operation;

[0241] The feasibility determination and correction module is used to determine the feasibility of the chromosome in the current population i...

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Abstract

The invention provides an intelligent optimization method and system for cooperative task allocation of an unmanned aerial vehicle and a vehicle, and relates to the technical field of unmanned aerial vehicles. The method comprises the steps: acquiring an unmanned aerial vehicle and vehicle cooperative task allocation model and preset parameters; constructing an initial population by adopting a vehicle-machine collaborative hybrid coding method; calculating the fitness value of the chromosome, and then operating the chromosome; judging the feasibility of chromosome corresponding solutions; correcting the chromosomes which do not pass the feasibility judgment; updating chromosomes in the current population to generate a filial generation population; if the maximum number of iterations is not reached, carrying out the chromosome operation again; if yes, outputting the scheme corresponding to the chromosome with the maximum fitness value in the offspring population. According to the method, a line segment task can be ensured to be uniquely accessed, and a large number of infeasible chromosomes can be avoided in a chromosome updating operation process, so that the genetic algorithm provided by the invention saves the time for judging the infeasible chromosomes and correcting the infeasible chromosomes.

Description

Technical field [0001] The present invention relates to the field of proactive technology, and more particularly to a smart optimization method and system for unmanned and vehicle synergistic tasks. Background technique [0002] UAV and vehicle collaborative implementation tasks have been widely used in the fields such as logistics distribution, traffic patrol, and power inspection, compared to the use of only vehicles and only drones implement tasks, drones and The task allocation problem between the vehicles began to receive attention and research. Under this issue, it is necessary to optimize the task allocation scheme of the drone and the vehicle to minimize the total time of the completion task. At the same time, it is subject to constraints such as life, task type, and road network network. Challenge, and with the increase of the task, the variables and constraints of the model are exponentially grow, and it is difficult to use the exact algorithm to effectively solve this ...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06N3/12
CPCG06Q10/063112G06N3/126Y02T10/40
Inventor 罗贺李娅吕欠伟陈盈盈王国强胡笑旋李晓多曹欣程鹏飞朱默宁靳鹏马华伟夏维唐奕城
Owner HEFEI UNIV OF TECH