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A multi-step optimization method for UAV swarm area coverage based on ant colony algorithm

An ant colony algorithm and area coverage technology, which is applied to the multi-step optimization of area coverage of UAV swarms and track planning problems, to achieve the optimal effect of real-time coverage area

Active Publication Date: 2020-06-05
XIDIAN UNIV
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AI Technical Summary

Problems solved by technology

However, the problem of robot coverage is not fully applicable to the problem of using unmanned early warning aircraft fleet to achieve the maximum real-time coverage of the area, so further improvements need to be made on the basis of the current research on coverage problems to meet the needs of using distributed airborne radar systems for multiple base processing needs

Method used

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  • A multi-step optimization method for UAV swarm area coverage based on ant colony algorithm
  • A multi-step optimization method for UAV swarm area coverage based on ant colony algorithm
  • A multi-step optimization method for UAV swarm area coverage based on ant colony algorithm

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Embodiment Construction

[0026] refer to figure 1 , is a flow chart of a multi-step optimization method for regional coverage of unmanned aerial vehicles based on an ant colony algorithm of the present invention; wherein the multi-step optimization method for regional coverage of unmanned aerial vehicles based on ant colony algorithm comprises the following steps:

[0027] Step 1. Set up the system simulation environment: first, set the monitoring area S of the UAV group, the feasible flight area A of the UAV group, and the maximum turning angle when each UAV is overloaded during the turning process. more than θ m In the case of , the UAV swarm needs to maximize the coverage of the surveillance area S of the UAV swarm without flying out of the feasible area A of the UAV swarm, θ m Indicates the maximum turning angle of the UAV speed; then, set the airborne radar operating parameters, including the peak power and antenna gain of the airborne radar, and then set the number of UAVs included in the UAV g...

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Abstract

The invention discloses a multi-step optimization method of unmanned aerial vehicle group region coverage based on an ant colony algorithm. The method comprises the following steps: setting the total number of ants in an ant colony algorithm and the number of dimensions of each ant; letting s represent an sth search, wherein s belongs to [1, SearchNum], SearchNum represents the number of searches, and iteration is carried out D times for each search; letting d represent a dth iteration, wherein d belongs to [0, D]; calculating a first data structure AntSwarm (s, d) after the dth iteration of the sth search and a second data structure OptSwarm (s, d) before the dth iteration of the sth search; adding 1 to d until a second data structure OptSwarm (s, D) before the Dth iteration of the sth search is obtained, and getting a complete optimal path of an unmanned aerial vehicle group after the sth search; and adding 1 to s until a complete optimal path of the unmanned aerial vehicle group after a (SearchNum)th search is obtained, and completing a multi-step optimization process of unmanned aerial vehicle group region coverage based on the ant colony algorithm, wherein the complete optimal path of the unmanned aerial vehicle group after the (SearchNum)th search includes the optimal path pSearchNum of the unmanned aerial vehicle group marching forward InnerStep steps after the (SearchNum)th search and the optimal velocity vSearchNum of the unmanned aerial vehicle group marching forward InnerStep steps after the (SearchNum)th search.

Description

technical field [0001] The invention belongs to the field of airborne radar technology, and in particular relates to a multi-step optimization method for area coverage of unmanned aerial vehicle groups based on an ant colony algorithm, which is applicable to the track planning problem of real-time optimal coverage monitoring area of ​​unmanned early warning aircraft groups. Background technique [0002] Unmanned Aerial Vehicle (Unmanned Aerial Vehicle), referred to as UAV, has unique advantages compared with manned aircraft, such as small size, low cost, easy to use, strong battlefield survivability, and no need to consider the physiological limitations of pilots. It plays an irreplaceable role in critical and highly dangerous tasks; with the rapid development of electronic technology, UAVs have been used in military fields such as reconnaissance and surveillance, electronic countermeasures and even precision strikes; therefore, pre-planning for UAVs A trajectory with the le...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05D1/12
CPCG05D1/12
Inventor 王彤胡艳艳刘嘉昕李杰
Owner XIDIAN UNIV
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