Unmanned aerial vehicle cooperative flight path planning method based on Bayesian and evolutionary algorithms

Abstract

The invention provides an unmanned aerial vehicle cooperative flight path planning method based on Bayesian and evolutionary algorithms, and mainly solves the problems of high optimization difficultyand low optimization efficiency of unmanned aerial vehicle cooperative flight path planning in the prior art. According to the scheme, the method includes: finding out a group of identical or similarnodes by calculating the distance between two track nodes, and then dividing the two tracks into a group of exchangeable track unit pairs by taking the nodes as boundaries; calculating the change intensity of the exchanged track length of all track unit pairs so as to determine the exchange intensity of the track unit pairs; and finally, determining the exchange probability of the flight path units by using Bayesian reasoning according to the exchange intensity of the flight path unit pairs to obtain the total cooperative cost of the unmanned aerial vehicle. According to the method, in unmanned aerial vehicle cooperative flight path planning tasks with various threat costs, the generation of flight path breakpoints can be effectively avoided, the convergence of optimization is improved, the flight path optimization of the unmanned aerial vehicle can be achieved more efficiently, and higher optimization performance is obtained.

Description

Technical field[0001]The invention belongs to the technical field of information processing, and relates to the navigation trajectory planning of an unmanned aerial vehicle, in particular to a coordinated trajectory planning method for an unmanned aerial vehicle based on Bayesian and evolutionary algorithms, which can be used for trajectory planning of an unmanned aerial vehicle in three-dimensional space.Background technique[0002]Multi-UAV coordinated flight is an important trend in the development of UAVs in the future, and trajectory planning is a key link in UAV flight. The coordinated flight of multiple drones puts forward higher requirements on the trajectory planning problem. It not only needs to consider the various external threats faced by the drone during flight and the limitation of its own trajectory length, but also need to consider multiple unmanned aerial vehicles. The overall collaboration of the machine.[0003]In order to better solve the problem of coordinated plan...

AI Technical Summary

Problems solved by technology

It is difficult to intersect the trajectory represented by antibodies, because the trajectory is a line connected by some grid points in three-dimensional space. If an inappropriate intersection operation is used, it may cause large breakpoints in the trajectory. And these breakpoints are difficult to deal with, which will seriously affect the track optimization; in addition, in the process of crossing tracks, treating all track segments without distinction will reduce the efficiency of track optimization

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