Design method of low-altitude public air route network of unmanned aerial vehicle

Abstract

The invention discloses a design method of low-altitude public air route network of an unmanned aerial vehicle, and belongs to the technical field of air route planning. The method includes the following steps that unmanned-aerial-vehicle low-altitude flight environment data in a planning area is obtained; according to the unmanned-aerial-vehicle low-altitude flight environment data, unmanned-aerial-vehicle airport layout information is obtained, wherein the unmanned-aerial-vehicle airport layout information includes location sites of multiple UAV airports and service scopes of the UAV airports; according to the unmanned-aerial-vehicle airport layout information, the unmanned-aerial-vehicle low-altitude flight environment data and the ant colony algorithm, several three-dimensional routesare obtained; according to the several three-dimensional routes, the low-altitude public air route network of the unmanned aerial vehicle is formed. According to the technical scheme, the low-altitudepublic air route network of the unmanned aerial vehicle is constructed, the path search efficiency is higher when an air route is constructed, and less time is consumed.

Description

technical field [0001] The invention belongs to the technical field of unmanned aerial vehicles, and in particular relates to a design method for an unmanned aerial vehicle low-altitude public route network. Background technique [0002] The trajectory planning of UAVs relies on space partitioning methods and trajectory planning algorithms. At present, the space division of trajectory planning mainly adopts unit decomposition, which refers to an efficient method for discretizing the flying airspace environment of UAVs. This method divides the environment into free units and obstacle units, and uses discrete methods to represent the environment; unit The representation methods include Voronoi diagram method and grid method. Among them, the Voronoi diagram constructs the initial optional path set or sets the navigation nodes, and then selects the appropriate path through the optimization algorithm. The disadvantage is that the determination of the location and number of navig...

AI Technical Summary

Problems solved by technology

The ant colony algorithm successfully and efficiently solves the traveling salesman problem, and has advantages in solving complex optimization problems, especially discrete optimization problems, but at the same time, it also has problems such as slow convergence speed and easy to fall into local optimum.

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