文档介绍：摘 要
无人机作为现代战场中的新一代武器，引起了许多国家的重视，而航迹规划
算法是无人机成功完成各项任务的重要保证。
为了验证改进后的粒子群算法在航迹规划中的有效性，本论文在一类粒子群
优化算法和环境建模的基础上，对无人机航迹规划进行了仿真实验。本论文的主
要研究内容包括以下三部分。
本文以两种常用来测试优化算法性能的 benchmark 函数对一类粒子群算法进
行了性能测试，并对具有固定加速因子的算法用线性和非线性的变加速因子来进
行了性能测试。由于现有算法收敛率低、稳定性差等缺点，本文提出了三种改进
的粒子群算法 GQPSO、C-GQPSO 和 SAC-GQPSO，并通过实验数据与现有算法进
行了比较；
本文完成了对航迹规划的环境建模，其中包括基准地形、障碍区域和威胁区
域的建模。随后本文对航迹路线进行了建模，在建立航迹评价函数后以其为目标
函数通过粒子群算法进行搜索，并以搜索到的最优粒子来建立航路点从而完成对
航迹路线的建模；
本文以一类粒子群算法作为航迹规划算法对无人机航迹规划进行了仿真实
验，并在不同最小转弯角和最低飞行高度的条件下以改进的 SAC-GQPSO 算法进
行了实验。最后本文通过实验数据对由现有算法和改进算法得到的航迹路线进行
了比较，并验证了由于以现有算法得到的航迹路线评价值均值大、标准差高等缺
点而提出的三种改进粒子群算法在无人机航迹规划中的适用性和有效性。
关键词：无人机，航迹规划，粒子群优化算法，量子粒子群优化算法
I
ABSTRACT
As a new generation of weapon in morden warfare, Unmanned Aerial Vehicle
(UAV) has caught attention of many countries in the world. Algorithms for path
planning are the key to ensure that UAV can complete different missions successfully.
For the purpose of verifying that particle swarm optimization (PSO) has
effectiveness in path planning for UAV, this thesis carried out several simulation
experiments on path planning for UAV. The experiments are based on a class of particle
swarm optimization algorithms and a environmental model. The main content of this
thesis includes three parts as follows.
This thesis carried out several performance tests on a class of particle swarm
optimization algorithms using two different benchmark functions which are often used
to test the performance of optimization algorithms. This thesis also tested the
performance of the algorithms by changing the constant acceleration factors with linear
and non-linear acceleration factors. Because of the defects of the existing algorithms
such as a low rate of convergence and a poor stability, this thesis presented three
modified algorithms which