摘要
基于促使智能飞行器在飞行过程中进行定位误差校正,快速规划出最优航迹路径的目的,本文建立智能飞行器偏离航迹误差的非线性数学模型,并运用遗传模拟退火算法对所建立的模型进行求解,通过经典测试函数——Resenbrock函数对所提出的改进算法进行测试,证明改进算法的优越性,同时考虑飞行器的最小转弯半径,建立航迹规划模型,并运用粒子群算法对其进行求解,利用Mereopolis接受准则产生最优飞行器航迹。通过实验仿真分析,飞行器达到了航迹长度尽可能小且经过校正区域进行校正的次数尽可能少的优化目标,验证了本文建立的函数模型和采用的算法具有可行性和有效性。
The aim of fast planning the optimal flight path is based on the position error correction of the intelligent aircraft during the flight.A nonlinear mathematical model of the deviation track error of intelligent aircraft is established,and a genetic simulated annealing algorithm is used to solve the model.The improved algorithm is tested by the classical test function Resenbrock function,the superiority of the improved algorithm is proved.By considering the minimum turning radius of the aircraft,a route planning model is established,which is solved by particle swarm optimization algorithm.Through the experimental simulation analysis,the flight path length is as small as possible and the number of correction times in the correction area is as small as possible,the feasibility and effectiveness of the function model and the algorithm are verified.
作者
周围
周元华
李旭
ZHOU Wei;ZHOU Yuanhua;LI Xu(Guangdong Nonferrous Metal Geological Exploration Institute,Guangzhou 510089,China;Guangdong Geological Survey and Mapping Institute,Guangzhou 510800,China;School of Surveying,Mapping&Geoinformation,Guilin University of Technology,Guilin 532100,China)
出处
《测绘与空间地理信息》
2022年第7期127-130,共4页
Geomatics & Spatial Information Technology
关键词
多约束条件
航迹规划
遗传模拟退火算法
粒子群算法
multi-constraints
flight route planning
genetic simulated annealing algorithm
particle swarm optimization algorithm
