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Aircraft flight path rapid planning optimization method based on genetic algorithm

A genetic algorithm and optimization method technology, applied in the field of aircraft track optimization, can solve problems such as mission failure and inability of the positioning system to accurately locate

Active Publication Date: 2020-09-25
HANGZHOU DIANZI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the limitations of the system structure of intelligent aircraft, the positioning system of this type of aircraft cannot accurately locate itself. Once the positioning error accumulates to a certain extent and exceeds a certain potential threshold, it is likely to cause mission failure.

Method used

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  • Aircraft flight path rapid planning optimization method based on genetic algorithm
  • Aircraft flight path rapid planning optimization method based on genetic algorithm
  • Aircraft flight path rapid planning optimization method based on genetic algorithm

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Embodiment 1

[0078] This embodiment provides a method for fast planning and optimization of aircraft track based on genetic algorithm, such as figure 1 shown, including steps:

[0079] S11. Establishing the objective function and constraint conditions of multi-objective vehicle route planning;

[0080] S12. Establishing a first model for multi-objective aircraft route planning according to the objective function and constraints;

[0081] S13. Based on the non-dominated sorting genetic algorithm NSGA-II, the established first model is solved to obtain the optimal solution of the flight path planning.

[0082] This embodiment requires that the best calibration flight route be drawn up for the intelligent aircraft to ensure that its position error can be corrected in time during the flight and continue to fly according to the specified track. At the same time, the proposed route also needs to meet the corresponding constraints of the problem. Make the track length of the aircraft as small a...

Embodiment 2

[0141] The differences between the genetic algorithm-based fast planning and optimization method for aircraft track provided in this embodiment and the first embodiment are:

[0142] In this embodiment, on the basis of the first model, the trajectory planning of the aircraft with the turning radius is considered, and the second model is obtained. The specific steps are:

[0143] S11. Establishing the objective function and constraint conditions of multi-objective vehicle route planning;

[0144] S12. Establishing a first model for multi-objective aircraft route planning according to the objective function and constraints;

[0145] S13. Add the constraint condition of the minimum turning radius of the aircraft itself into the first model to obtain the second model of the route planning of the multi-objective aircraft;

[0146] S14. Solve the established second model based on the non-dominated sorting genetic algorithm NSGA-II, and obtain an optimal solution for flight path pla...

Embodiment 3

[0177] The differences between the genetic algorithm-based fast planning and optimization method for aircraft track provided in this embodiment and the first embodiment are:

[0178] This embodiment considers the flight loop that will change dynamically over time. Even if the correction points have been determined before the flight of the aircraft, the aircraft may not be able to achieve the ideal correction when performing error correction at some correction points (abbreviated as abnormal points). The error is precisely corrected to 0. The probability that the abnormal point can successfully correct the error of a certain state to 0 is 80%. If the correction fails, the remaining error after correction is min(error 校正前 ,5) units. Considering that there is a correction failure in the abnormal point, the probability of successfully reaching the terminal is as large as possible, and a three-objective optimization model is established (the track is as short as possible, the numb...

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Abstract

The invention discloses an aircraft route rapid planning optimization method based on a genetic algorithm. The method comprises the following steps: S11, establishing an objective function and constraint conditions of multi-objective aircraft route planning; S12, establishing a first model of multi-target aircraft route planning according to the target function and the constraint condition; and S13, solving the established first model based on a non-dominated sorting genetic algorithm NSGA-II to obtain an optimal solution of route planning. According to the method, an aircraft route planning problem in a three-dimensional space is converted into a multi-target vehicle route planning problem, and complex aircraft space route planning is converted into solving of the shortest route of a given point under multiple constraints, so that the calculation complexity is simplified, and the universality of the model is improved.

Description

technical field [0001] The invention relates to the technical field of aircraft track optimization, in particular to a genetic algorithm-based rapid planning and optimization method for aircraft track. Background technique [0002] Smart Aircraft (Smart Aircraft, SA), derived from the prototype of Unmanned Aerial Vehicle (UAV), is a new type of tool equipment capable of long-distance flight and independent task execution, and is widely used in aerial photography, agriculture Common civilian fields such as plant protection, disaster relief, and delivery of supplies, as well as military fields such as threat detection, enemy target search, ground attack, and target bombing. [0003] In real life, with the rapid development of intelligent aircraft technology, controlling the rapid planning of its trajectory in complex environments has become an important hot topic in this field. Looking back at the relevant literature on the flight path optimization of intelligent aircraft in ...

Claims

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Application Information

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IPC IPC(8): G01C21/22G06N3/12
CPCG01C21/22G06N3/126
Inventor 黄桁王帅吴倩倩陶俐言
Owner HANGZHOU DIANZI UNIV
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