Unmanned aerial vehicle flight path planning method based on improved clustering algorithm

A technology of track planning and clustering algorithm, which is applied to computer parts, computing, navigation computing tools, etc. It can solve the problems of large amount of calculation and low accuracy, and achieve the goal of small amount of calculation, high accuracy, and enhanced exploration and efficiency. The effect of mining

Active Publication Date: 2020-04-10
HARBIN INST OF TECH
View PDF8 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to solve the problem of low accuracy and large amount of calculation caused by clustering for each track in the existing multi-objective track planning method, and proposes an unmanned algorithm based on an improved clustering algorithm. Aircraft Trajectory Planning Method

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Unmanned aerial vehicle flight path planning method based on improved clustering algorithm
  • Unmanned aerial vehicle flight path planning method based on improved clustering algorithm
  • Unmanned aerial vehicle flight path planning method based on improved clustering algorithm

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0042] Specific implementation mode one: the specific process of a UAV trajectory planning method based on an improved clustering algorithm in this implementation mode is as follows:

[0043] Problem description of UAV trajectory planning

[0044] UAVs have demonstrated their advantages and potential in military and civilian fields. In order to achieve autonomous navigation of UAVs, aspects such as modeling, trajectory planning, and control system design must be considered. Among them, trajectory planning is an important problem that has been extensively studied.

[0045]UAV trajectory planning is to find the optimal or suboptimal trajectory from the starting point to the destination under the condition of satisfying multiple constraints (including UAV attributes and terrain constraints). The quality of the flight path is measured by indicators such as flight length, flight altitude, and probability of being destroyed. Therefore, the trajectory planning problem can be descr...

specific Embodiment approach 2

[0109] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that the expression for calculating the P target value in the step 1 is:

[0110]

[0111] x=(x 1 ,y 1 ,z 1 ,...,x w ,y w ,z w ) T

[0112] s.t.x j ∈[x a ,x b ],y j ∈[y a ,y b ],z j ∈[z a ,z b ],j=1,...,w

[0113] where x is x 1 or x N , f 1 (x) is the minimum total track length, f 2 (x) is the total flight altitude, g k is the constraint function, k is the number of clusters; x 1 ,y 1 ,z 1 is the coordinate value of the first path point, x w ,y w ,z w is the coordinate value of the wth path point, w is the number of paths in the track; x j ,y j ,z j is the coordinate value of the jth waypoint, and j is the serial number of the waypoint in the track; [x a ,x b ],[y a ,y b ] and [z a ,z b ] denote the lower and upper bounds for the x, y and z coordinates, respectively.

specific Embodiment approach 3

[0114] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is that in the step three, a clustering algorithm is used to find the neighbor path point S of each path point, S=Clustering (P, w, K); The specific process is:

[0115] Step 31. Set the population P, the number of path points w, and the maximum number of clusters K;

[0116] Let j = 2;

[0117] Step 32. Find the waypoints with sequence number j in all tracks to form global waypoints

[0118] In the formula, is the x-coordinate value of the jth waypoint of the first track, is the x-coordinate value of the jth waypoint of the Nth track, is the y-coordinate value of the jth waypoint of the first track, is the y-coordinate value of the jth waypoint of the Nth track, is the z-coordinate value of the jth waypoint of the first track, is the z-coordinate value of the jth waypoint of the Nth track;

[0119] Step 33, use the K-means algorithm to convert the global...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an unmanned aerial vehicle flight path planning method based on an improved clustering algorithm, and aims to solve the problems of low accuracy and large calculation amount caused by clustering of each flight path in an existing multi-target route planning method. The method comprises the steps of 1, setting a differential evolution operator control parameter, a maximum clustering number and a mating limit probability, generating an initial population and calculating a target value, and establishing an external document; 2, setting the number of iterations to be T, andenabling t to be equal to 1; 3, finding a neighbor path point of each path point; 4, setting the number of flight paths, and enabling i to be equal to 1; 5, generating a new flight path; 6, calculating a target value of the new flight path; 7, storing the better flight path in the external document; 8, enabling i to be equal to i+1, and repeating the steps 4-8 until i is equal to N; and 9, updating the population, setting t to be equal to t+1, and repeatedly executing the steps 2-9 until t is equal to T. The method is applied to the field of unmanned aerial vehicle flight path planning.

Description

technical field [0001] The invention relates to an unmanned aerial vehicle track planning method based on an improved clustering algorithm. Background technique [0002] So far, scholars have proposed a variety of trajectory planning methods, such as A* algorithm, D*Lite algorithm, bi-level programming algorithm, grid-based algorithm and intelligent computing method. [0003] Although a large number of methods have been proposed to solve the UAV trajectory planning problem, people usually express the trajectory planning problem as a single-objective optimization problem and propose a single-objective optimization algorithm. However, the trajectory planning problem usually has multiple conflicting goals, for example, we hope to obtain the minimum probability of being destroyed but at the same time we hope that the trajectory is the shortest. Existing methods for dealing with multiple conflicting objectives multiply each objective by a coefficient and then sum these weighted ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/20G06K9/62
CPCG01C21/20G06F18/23213
Inventor 宋申民李欣刘庭瑞杨小艳
Owner HARBIN INST OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap