A kind of route planning method for UAV based on improved PSO algorithm

Author

Qingbo Geng ; Zheng Zhao

Author_Institution

Sch. of Autom., Beijing Inst. of Technol., Beijing, China

fYear

2013

fDate

25-27 May 2013

Firstpage

2328

Lastpage

2331

Abstract

This paper studies the basic model of the UAV track planning. As basic Particle Swarm Optimization (PSO) algorithm is easy to fall into local minimum and its searching accuracy is not ideal, the author puts forward an improved hybrid particle swarm UAV route planning method with contraction factor. This method is used to change algorithm in the balance of performance by introducing contraction factor and learning factor, in order to get a better convergence speed and convergence rate. At the same time using MATLAB as the development tool for simulation, the results show that this method is simple and effective, and can meet the requirements of the UAV path planning.

Keywords

autonomous aerial vehicles; learning (artificial intelligence); particle swarm optimisation; path planning; search problems; MATLAB; UAV path planning; UAV track planning; contraction factor; convergence rate; convergence speed; hybrid particle swarm UAV route planning method; improved PSO algorithm; learning factor; particle swarm optimization algorithm; Algorithm design and analysis; Convergence; Heuristic algorithms; Particle swarm optimization; Path planning; Planning; Unmanned aerial vehicles; Improved hybrid particle swarm; UAV; path planning;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2013 25th Chinese

Conference_Location

Guiyang

Print_ISBN

978-1-4673-5533-9

Type

conf

DOI

10.1109/CCDC.2013.6561326

Filename

6561326