Title :
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
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;
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561326
