Multi-unmanned aerial vehicle area coverage deployment method based on particle swarm genetic algorithm

A genetic algorithm and multi-UAV technology, applied in the field of multi-UAV regional coverage deployment based on particle swarm genetic algorithm, can solve the uneven deployment of UAVs, and it is difficult to ensure UAV network connectivity and regional coverage rate is difficult to guarantee and other issues

Active Publication Date: 2019-09-13
NANJING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Technical problem: In the multi-UAV regional coverage deployment, if the UAVs are randomly deployed, it is difficult to ensure the connectivity of the UAV network. In addition, the random deployment method may make the UAV deployment uneven, resulting in regional It is also difficult to guarantee the coverage, so the connectivity of the network and the coverage of the area must be considered when studying the deployment of multi-UAV regional coverage

Method used

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  • Multi-unmanned aerial vehicle area coverage deployment method based on particle swarm genetic algorithm
  • Multi-unmanned aerial vehicle area coverage deployment method based on particle swarm genetic algorithm
  • Multi-unmanned aerial vehicle area coverage deployment method based on particle swarm genetic algorithm

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Embodiment 1

[0053] A method for deploying multi-UAV area coverage based on particle swarm genetic algorithm, comprising the following steps:

[0054] Step 1) Input the length L and width W of the target area;

[0055] Step 2) Input the number of UAVs Total, sensor deployment angle θ, sensor coverage radius Rs and maximum communication radius Rt, traverse each UAV U ξ , enter UAV U ξ the height H ξ ;

[0056] Step 3) Initialize the empty deployment plan set PList, randomly deploy UAVs within the range of length L and width W, and determine whether the UAV cluster is connected. When the UAV cluster is connected, the current UAV Deployment scheme P i Save in the middle of the deployment scheme collection PList, repeat this step until the number of collections in the deployment scheme collection PList is equal to N, and said N represents the number of individuals in the initial population;

[0057] Step 4) calculate the area coverage of each scheme in the collection PList, described cove...

Embodiment 2

[0093] A method for deploying multi-UAV area coverage based on particle swarm genetic algorithm, comprising the following steps:

[0094] Step 1) Input the range of the target area 100m×100m, such as figure 2 shown.

[0095] Step 2) Input the number of UAVs Total31, the UAV sensor coverage radius Rs20 meters, the UAV sensor deployment angle θ90 degrees, and the UAV maximum communication radius Rt 35 meters, such as image 3 As shown, in order to facilitate the description of the coverage problem, it is set that the flying height of all UAVs is 16 meters when performing tasks, and the coverage radius of all UAVs on the ground is 12 meters.

[0096] Step 3) Under the premise of ensuring network connectivity, randomly deploy UAVs to obtain a set of 40 schemes. Set deployment plan is a random scheme that satisfies network connectivity.

[0097] Step 4) Calculate the area coverage of the deployment scheme, and divide the target area into 10,000 1×1 small square grids. It can...

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Abstract

The invention discloses a multi-unmanned aerial vehicle area coverage deployment method based on a particle swarm genetic algorithm, and solves the problem of area coverage deployment of an unmanned aerial vehicle according to the size of a target area and various parameters of the unmanned aerial vehicle. According to the method, the particle swarm algorithm is used as a basic framework, and theimproved genetic algorithm is embedded into the iteration process of the particle swarm algorithm, so that the algorithm is prevented from falling into a local extreme value. According to the method,a particle swarm genetic algorithm is used to carry out comparative study on the coverage deployment scheme according to the regional coverage rate and the network connectivity, and multiple times ofiterative optimization is carried out to finally obtain an optimal coverage deployment scheme.

Description

technical field [0001] The present invention relates to the field of topology control, in particular to a multi-unmanned aerial vehicle area coverage deployment method based on particle swarm genetic algorithm. Background technique [0002] In the implementation of multi-UAV self-organization tasks such as reconnaissance behind enemy lines, post-disaster rescue and forest fire prevention, it is also crucial to monitor the target area, so the patent of this invention will focus on the multi-UAV self-organization area coverage control, research and Explore how to deploy UAV nodes reasonably to maximize the area coverage under the premise of ensuring network connectivity when the number of UAVs is limited. In the process of optimizing the problem, the particle swarm optimization algorithm continuously adjusts its own moving direction through the learning of the individual extreme value and the group extreme value, and solves the optimal solution of the problem through continuou...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04B7/185H04W16/18
CPCH04B7/18506H04W16/18
Inventor 陈志王福星岳文静汪皓平狄小娟
Owner NANJING UNIV OF POSTS & TELECOMM
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