摘要
为了提升无人飞行器(UAV)在三维环境中对气味源的定位能力,提出了一种改进麻雀搜索算法(CE-SSA)。该算法通过引入精英策略、拥挤因子和莱维飞行扰动机制,增强了搜索能力并有效避免了陷入局部最优。实验中,模拟了三维空间中气味羽流的扩散,并将CE-SSA与经典粒子群算法(PSO)和原始麻雀搜索算法(SSA)进行了对比。结果表明,在单个无人机的情况下,CE-SSA的定位误差比传统算法降低了98%以上,定位成功率提高了56%以上;当无人机数量达到4架及以上时,定位误差稳定在0.2 m以下,成功率为100%。此外,CE-SSA在不同气味羽流特征下展现了较强的鲁棒性,能够应对复杂的环境变化。研究表明,CE-SSA不仅在提升定位精度和成功率方面具有显著优势,而且为无人机在复杂环境中的气味源追踪技术提供了可靠的解决方案。该研究不仅为主动嗅觉技术发展做出了贡献,也突显了无人机在复杂场景中进行气味源追踪的潜力。
In order to enhance the odor source localization capability of unmanned aerial vehicles(UAVs)in three-dimensional environments,a crowding factor-elitist strategy improved sparrow search algorithm(CE-SSA)is proposed.This algorithm incorporates elite strategy,crowding factor,and Lvy flight perturbation mechanisms to improve search capability and effectively avoid local optima.In the experiments,the diffusion of the odor plume in a 3D space was simulated,and the performance of CE-SSA was compared with that of the classical particle swarm optimization(PSO)and the original sparrow search algorithm(SSA).The results show that,in the case of a single UAV,CE-SSA reduces localization error by over 98%compared to traditional algorithms and increases the success rate by more than 56%.When the number of UAVs reaches four or more,the localization error stabilizes below 0.2 meters,with a success rate of 100%.Moreover,CE-SSA demonstrates strong robustness under different odor plume characteristics and can handle complex environmental variations.The study indicates that CE-SSA offers significant advantages in improving localization accuracy and success rate,providing a reliable solution for UAV-based odor source tracking in complex environments.The findings of this research provide theoretical support for the further development of active olfactory technology and expand the potential applications of UAVs in environmental monitoring and disaster response.
作者
许顺
黄宇鼎
范靖
汤达恒
吕游
Xu Shun;Huang Yuding;Fan Jing;Tang Daheng;Lyu You(College of Engineering and Technology,Jilin Agricultural University,Changchun 130118,China;Changchun Satellite Observation Station,National Astronomical Observatories,Chinese Academy of Sciences,Changchun 130117,China)
出处
《电子测量与仪器学报》
北大核心
2025年第7期236-246,共11页
Journal of Electronic Measurement and Instrumentation
基金
吉林省科技发展计划项目(20250602019RC)
吉林省教育厅基础研究(JJKH20220334KJ)项目资助。
关键词
主动嗅觉技术
气味源定位
麻雀搜索算法
三维羽流建模
active olfaction technology
odor source localization
sparrow search algorithm
3D plume modeling
