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基于航迹规划的无人机地形辅助导航OA北大核心CSTPCD

Path planning-based terrain contour matching navigation of unmanned aerial vehicles

中文摘要英文摘要

为改善无人机基于地形轮廓匹配的地形辅助导航地形适应性差、实时性差的问题,本文提出一种基于航迹规划的地形辅助导航方法.针对地形匹配对地形依赖性强的问题,将无人机需要途经的区域进行分块并计算各块地形标准差,根据地形标准差选出地形平坦区域和地形起伏区域,用航迹规划算法为无人机规划出在地形起伏较大区域的航线.针对传统遍历搜索高程匹配算法耗时长的问题,在粒子群算法基础上引入分布估计的思想,形成优化粒子群算法进行地形轮廓匹配,加强全局寻优能力.仿真实验结果表明:优化粒子群算法降低匹配时长 2/3,基于航迹规划的地形辅助导航降低位置误差 6/7.

A terrain-aided navigation method based on track planning is proposed in this paper to improve the poor adaptability and real-time performance of unmanned aerial vehicle(UAV)terrain-aided navigation based on terrain contour matching.To solve the problem of terrain matching being highly dependent on terrain,the area that UAVs must approach is divided into blocks,and the terrain standard deviation of each block is calculated.The flat terrain area and the terrain undulating area are then selected based on the terrain standard deviation,while the flight track planning algorithm is used to plan the route for UAV in the area with large terrain undulating.The idea of distribu-tion estimation is introduced on the basis of the particle swarm optimization(PSO)algorithm with the aim of ad-dressing the time-consuming problem of traditional traversal search elevation matching algorithms.This proposed so-lution can form the optimized PSO algorithm for terrain contour matching and enhance global optimization ability.The simulation results show that the optimized PSO reduced the matching time by 2/3.Furthermore,the terrain-ai-ded navigation based on track planning improved matching accuracy by 6/7.

张睿;李万睿;肖勇;杨林;许斌

东南大学 自动化学院,江苏 南京 210096||西北工业大学 自动化学院,陕西 西安 710072西北工业大学 自动化学院,陕西 西安 710072成都飞机设计研究所,四川 成都 610041

地形辅助导航航迹规划优化粒子群算法地形轮廓匹配地形适应性群智能算法A*算法实时性

terrain aided navigationtrack planningoptimized particle swarm optimizationterrain contour matc-hingterrain adaptabilityswarm intelligence algorithmA* alogrithmreal-time capability

《哈尔滨工程大学学报》 2024 (003)

459-465 / 7

国家自然科学基金项目(61933010);江苏省卓越博士后计划项目(2022ZB117);航空科学基金项目(201905018002,201905018003).

10.11990/jheu.202206074

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