Unmanned aerial vehicle three-dimensional flight path planning method based on chaos adaptive sparrow search algorithm

Abstract

The invention discloses an unmanned aerial vehicle three-dimensional flight path planning method based on a chaos adaptive sparrow search algorithm. The method comprises the following steps: establishing a flight environment model according to a flight environment; establishing an unmanned aerial vehicle flight cost function, and evaluating the flight path performance of the unmanned aerial vehicle; improving a sparrow search algorithm by adopting a chaos initialized population strategy, a self-adaptive weight strategy and a Cauchy-Gaussian mixed variation strategy, and proposing a chaos self-adaptive sparrow search algorithm; and planning the flight path of the unmanned aerial vehicle in the three-dimensional environment by adopting a chaos adaptive sparrow search algorithm to obtain an optimal solution of the flight path planning of the unmanned aerial vehicle, and obtaining a planning result. The improved sparrow algorithm has obvious advantages in solving quality, a chaos strategy and an adaptive strategy of the improved sparrow algorithm enable the algorithm to have high convergence speed and excellent convergence precision, and a variation strategy of the improved sparrow algorithm enables the algorithm to have strong capability of jumping out of local optimum, so that an excellent unmanned aerial vehicle flight route can be quickly obtained.

Description

technical field [0001] The invention relates to the research field of unmanned aerial vehicle track optimization, in particular to a three-dimensional unmanned aerial vehicle track planning method based on a chaotic adaptive sparrow search algorithm. Background technique [0002] As we all know, UAV trajectory planning has become one of the important contents in defining UAV missions. The three-dimensional path planning required for UAV flight missions can be defined as a non-deterministic polynomial (NP) problem, and its main purpose is to optimize the path between the starting point and the target point. In this process, multiple constraints need to be dealt with, such as the UAV's own performance constraints, threat constraints, and environmental constraints. In order to make the planned trajectory meet the actual flyable requirements, the trajectory must be smoothed. In summary, the UAV 3D trajectory planning problem can be considered as a multi-constraint optimization...

AI Technical Summary

Problems solved by technology

However, in terms of disadvantages, when the SSA algorithm solves complex engineering optimization problems, it will start to appear "premature" phenomenon, resulting in low convergence accuracy and easy to fall into local optimum. Therefore, the standard SSA algorithm is used to directly solve the trajectory planning problem. low flyability

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