CN112269031B - 基于神经网络的旋翼无人机实时风速估计方法 - Google Patents
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Abstract
Description
技术领域
本发明涉及一种旋翼无人机实时风速估计方法。特别是涉及一种基于神经网络的旋翼无人机实时风速估计方法。
背景技术
旋翼无人机具有运动灵活且适应环境能力强等优势,常被用于科研考察、目标侦查[1]、喷洒农药[2]以及气味源定位[3]等应用场景。旋翼无人机在飞行中会受到风的作用和影响。然而,风不仅仅是旋翼无人机飞行过程中的主要干扰因素,也是无人机应用中的一种重要参考信息。例如旋翼无人机可通过室外环境中风速/风向信息实现气味源定位。地面移动机器人通过使用机载风速仪获取环境风速/风向信息[4],但对旋翼无人机而言,风速仪过于沉重,不便机载,并且无人机旋翼的高速转动会产生下洗气流,对其下方的气流场产生强烈扰动,从而很难从风速仪输出的复杂混合信号中提取准确的风速/风向信息。
作为一种飞行器,风速与风向对旋翼无人机的飞行参数(如飞行姿态与对地速度)存在显著影响,因此可通过使用旋翼无人机的飞行参数逆向估计其所在位置的风速/风向信息。现有的基于无人机的典型风矢量估计方法有基于旋翼无人机空气动力学模型的方法[5,6]、基于倾角测量的风速估计方法[7]、基于旋翼无人机动力学模型的方法[8]等。基于旋翼无人机空气动力学模型的方法[5,6]计算结果相对准确,但旋翼无人机的空气动力学模型过于复杂。基于倾角测量的风速估计方法[7]通过风洞标定实验,拟合出机载传感器惯性测量单元(IMU)所提供的飞行姿态与风速之间的关系,然后将该结果应用于当无人机悬停时的风速估计。该方法虽简单易用,但悬停会导致无人机飞行不连贯。王佳瑛和罗冰等人[8]利用四旋翼无人机动力学模型实现基于扩展状态观测器的风速估计,该方法在无人机悬停和飞行时均得到验证。不过该方法假设旋翼电机转速正比于其输入等效电压,并且四旋翼无人机的推力系数和阻力系数必须通过几个设计的标定实验进行获取。然而旋翼电机转速与其输入等效电压之间关系较为复杂,上述假设并不完全成立。若按照该假设进行近似计算,由此将引入系统误差。
发明内容
本发明所要解决的技术问题是,提供一种能够简便、准确地估计风速/风向的基于神经网络的旋翼无人机实时风速估计方法。
本发明所采用的技术方案是:一种基于神经网络的旋翼无人机实时风速估计方法,包括如下步骤:
1)进行标定实验,采集实验环境风速和旋翼无人机的飞行姿态角度、速度、加速度及旋翼电机的输入等效电压,并测量旋翼无人机质量;
2)根据旋翼无人机质量、旋翼无人机的飞行姿态角度和加速度,计算机体坐标系下每个采样时刻的总惯性力矢量;
3)根据实验环境风速、旋翼无人机的飞行姿态角度和速度计算机体坐标系下每个采样时刻的旋翼无人机空速矢量;
4)将机体坐标系下每个采样时刻的旋翼无人机空速矢量作为输出,旋翼电机的输入等效电压和机体坐标系下每个采样时刻的总惯性力矢量作为输入,对人工神经网络进行训练,训练完成后保存训练好的人工神经网络;
5)将实时采集的旋翼无人机飞行姿态角度、加速度计算当前时刻机体坐标系下的总惯性力矢量,将计算的总惯性力矢量和采集的旋翼电机的输入等效电压作为训练好的人工神经网络的输入,获得当前时刻机体坐标系下旋翼无人机空速矢量的估计值
本发明的基于神经网络的旋翼无人机实时风速估计方法,适用于任意旋翼数目的旋翼无人机,仅使用常规的机载传感器IMU与GPS,基于旋翼无人机的动力学模型,通过使用人工神经网络建立灰箱模型,由此避免使用多次标定实验确定旋翼无人机的推力系数和阻力系数,以及避免由于过度简化旋翼电机转速与其输入等效电压之间的关系而带来的系统误差,可简便、准确地估计风速/风向。本发明具有如下特色:
1、将旋翼无人机的动力学模型(解析方法)与人工神经网络(黑箱建模方法)相结合,形成灰箱模型;
2、方法简便、准确,可避免旋翼无人机的推力系数和阻力系数的确定,以及旋翼电机转速与其输入等效电压间的关系建模;
3、相比基于倾角测量的风速估计方法(需要悬停才能测量风速),本发明所提出的风速估计方法可以在飞行过程中实时测量三维风速/风向。
4、由于本发明所提出的风速估计方法是基于旋翼无人机的动力学模型并通过使用人工神经网络建立的灰箱模型,由此避免了由于过度简化旋翼电机转速与其输入等效电压之间的关系而带来的系统误差,因此与仅仅使用旋翼无人机动力学模型的方法相比,具有较高的测量精度。
5、本发明所提出的风速估计方法仅需要一次标定实验即可完成人工神经网络的训练,进而用于环境风速的测量。而现有的仅使用旋翼无人机动力学模型的方法需要进行多个不同的标定实验以确定旋翼无人机的推力系数和阻力系数。因此,本发明所提出的风速估计方法所需的标定实验更加简单易行。
附图说明
图1是四旋翼无人机的惯性坐标系I与机体坐标系B示意图;
图2是用于计算风速的速度三角形示意图。
具体实施方式
下面结合实施例和附图对本发明的基于神经网络的旋翼无人机实时风速估计方法做出详细说明。
本发明的基于神经网络的旋翼无人机实时风速估计方法,包括如下步骤:
1)在室外开阔环境下进行标定实验,采集实验环境风速和旋翼无人机的飞行姿态角度、速度、加速度及旋翼电机的输入等效电压,并测量旋翼无人机质量;
标定实验中采用的风速仪离旋翼无人机位置小于6m,这是因为在室外开阔环境下风场近似均匀,因此可用风速仪的测量值近似代替无人机处的风速。为了减少旋翼无人机对环境气流引起的扰动影响风速仪对风速的测量,风速仪需安装在无人机的上风向,旋翼无人机的飞行姿态角度和加速度由常规的机载惯性测量单元(IMU)传感器获得,速度由常规的机载GPS单元获得,旋翼电机的输入等效电压直接通过机载的飞行控制器的AD采样接口或电压测量单元获取。旋翼无人机质量通过称重获得。
2)根据旋翼无人机质量、旋翼无人机的飞行姿态角度和加速度,计算机体坐标系下每个采样时刻的总惯性力矢量;
所述的机体坐标系B下每个采样时刻的总惯性力矢量A为:
需要说明的是,旋翼无人机通常使用惯性坐标系I与机体坐标系B表示其位姿,在惯性坐标系I中,旋翼无人机位置可表示为在图1中,定义四旋翼无人机旋翼2和3连线的中点为无人机机头,定义机体坐标系B的Y轴(YB)正方向为四旋翼无人机的前进方向。在惯性坐标系I中,当四旋翼无人机机头方向朝北时(YI)偏航角为零。
3)根据实验环境风速、旋翼无人机的飞行姿态角度和速度计算机体坐标系下每个采样时刻的旋翼无人机空速矢量;
因此,机体坐标系下旋翼无人机空速矢量B为:
4)将机体坐标系下每个采样时刻的旋翼无人机空速矢量作为输出,旋翼电机的输入等效电压和机体坐标系下每个采样时刻的总惯性力矢量作为输入,对人工神经网络进行训练,训练完成后保存训练好的人工神经网络;
所述的旋翼电机的输入等效电压为Ui,i=1,2,3,…,N,N为旋翼无人机的旋翼个数,四旋翼为4,六旋翼为6,以此类推;人工神经网络可采用反向传播(BP)神经网络、径向基(RBF)神经网络等。这里以四旋翼无人机(N=4)、BP神经网络为例进行说明。BP神经网络采用经典的三层结构,包括输入层、隐藏层与输出层。选择Ui(i=1,2,3,4)和矢量A作为BP神经网络的输入,矢量B作为BP神经网络输出。矢量A和B的维数均为3。由此可知,输入层与输出层的节点个数分别为7和3,根据经验方法确定隐藏层单元个数为7。训练过程可使用数学工具软件离线进行,训练完成后保存神经网络(即保存神经元所用的激活函数、神经元间的连接权值等参数)。
5)将实时采集的旋翼无人机飞行姿态角度、加速度计算当前时刻机体坐标系下的总惯性力矢量,将计算的总惯性力矢量和采集的旋翼电机的输入等效电压作为训练好的人工神经网络的输入,获得当前时刻机体坐标系下旋翼无人机空速矢量的估计值
旋翼无人机在实际应用时,实时采集当前时刻的旋翼无人机飞行姿态角度、加速度和各旋翼电机的输入等效电压Ui(i=1,2,3,4),由(1)式计算当前时刻机体坐标系下的总惯性力矢量A,再将A与Ui(i=1,2,3,4)作为神经网络的输入,由神经网络进行计算获得当前时刻机体坐标系下旋翼无人机空速矢量的估计值这里所述的神经网络是指训练好的神经网络,在使用时通过加载由步骤4)完成后保存的神经网络参数(神经元所用的激活函数、神经元间的连接权值等)进行建立。上述计算过程可以在地面站计算机上进行(旋翼无人机将采集的各传感数据通过常规的机载数传模块发送到地面站),也可以在旋翼无人机机载的嵌入式计算机上进行。
参考文献如下:
[1]Eisenbeiss H,Sauerbier M.Investigation of UAV systems and flightmodes for photogrammetric applications[J].Photogrammetric Record,2015,26(136):400-421.
[2]Meivel S,Maguteeswaran R,Gandhiraj N,et al.Quadcopter UAV BasedFertilizer and Pesticide Spraying System[J].International Academic ResearchJournal of Engineering Sciences,2016,1(1):8-12.
[3]Neumann P,Bennetts V H,Lilienthal A,et al.Gas source localizationwith a micro-drone using bio-inspired and particle filter-based algorithms[J].Advanced Robotics,2013,27(9):725-738.
[4]Qing-Hao M,Fei L.Review of Active Olfaction[J].Robot,2006,28(1):89-96.
[5]Hoffmann G M,Huang H,Waslander S L,et al.Quadrotor HelicopterFlight Dynamics and Control:Theory and Experiment[M].American Institute ofAeronautics and Astronautics,2007.
[6]Huang H,Hoffmann G M,Waslander S L,et al.Aerodynamics and controlof autonomous quadrotor helicopters in aggressive maneuvering[C].Proceedingsof IEEE International Conference on Robotics and Automation,2009:3277-3282.
[7]Neumann P P,Bartholmai M.Real-time wind estimation on a microunmanned aerial vehicle using its inertial measurement unit[J].Sensors&Actuators A Physical,2015,235:300-310.
[8]Wang J Y,Luo B,Zeng M,et al.A Wind Estimation Method with anUnmanned Rotorcraft for Environmental Monitoring Tasks[J].Sensors,2018,18(12):1-20.
Claims (4)
1.一种基于神经网络的旋翼无人机实时风速估计方法,其特征在于,包括如下步骤:
1)进行标定实验,采集实验环境风速和旋翼无人机的飞行姿态角度、速度、加速度及旋翼电机的输入等效电压,并测量旋翼无人机质量;
2)根据旋翼无人机质量、旋翼无人机的飞行姿态角度和加速度,计算机体坐标系B下每个采样时刻的总惯性力矢量;
所述的机体坐标系B下每个采样时刻的总惯性力矢量A为:
3)根据实验环境风速、旋翼无人机的飞行姿态角度和速度计算机体坐标系下每个采样时刻的旋翼无人机空速矢量;
4)将机体坐标系下每个采样时刻的旋翼无人机空速矢量作为输出,旋翼电机的输入等效电压和机体坐标系下每个采样时刻的总惯性力矢量作为输入,对人工神经网络进行训练,训练完成后保存训练好的人工神经网络;
5)将实时采集的旋翼无人机飞行姿态角度、加速度计算当前时刻机体坐标系下的总惯性力矢量,将计算的总惯性力矢量和采集的旋翼电机的输入等效电压作为训练好的人工神经网络的输入,获得当前时刻机体坐标系下旋翼无人机空速矢量的估计值
2.根据权利要求1所述的基于神经网络的旋翼无人机实时风速估计方法,其特征在于,其特征在于,步骤1)中:
标定实验中采用的风速仪离旋翼无人机位置小于6m,风速仪安装在无人机的上风向。
4.根据权利要求1所述的基于神经网络的旋翼无人机实时风速估计方法,其特征在于,步骤4)中所述的旋翼电机的输入等效电压为Ui,i=1,2,3,…,N,N为旋翼无人机的旋翼个数,人工神经网络采用反向传播神经网络或径向基神经网络。
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Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP2390670A2 (en) * | 2010-05-27 | 2011-11-30 | Honeywell International Inc. | Wind estimation for an unmanned aerial vehicle |
CN102607639A (zh) * | 2012-02-24 | 2012-07-25 | 南京航空航天大学 | 基于bp神经网络的大攻角飞行状态下大气数据测量方法 |
CN106844887A (zh) * | 2016-12-29 | 2017-06-13 | 深圳市道通智能航空技术有限公司 | 旋翼无人机的动力学建模方法及装置 |
CN106885918A (zh) * | 2017-02-10 | 2017-06-23 | 南京航空航天大学 | 一种面向多旋翼飞行器的多信息融合实时风速估计方法 |
WO2019071327A1 (en) * | 2017-10-11 | 2019-04-18 | Embraer S.A. | NEURONAL NETWORK SYSTEM HAVING TRAINING BASED ON A COMBINATION OF MODEL AND FLIGHT INFORMATION FOR ESTIMATING AIRCRAFT AIR DATA |
CN110726851A (zh) * | 2019-12-02 | 2020-01-24 | 南京森林警察学院 | 一种利用旋翼无人机测算风速的方法 |
CN111176263A (zh) * | 2020-01-23 | 2020-05-19 | 北京航天自动控制研究所 | 一种基于bp神经网络的飞行器推力故障在线辨识方法 |
CN111758034A (zh) * | 2019-05-31 | 2020-10-09 | 深圳市大疆创新科技有限公司 | 风速确定方法、系统、飞行器及计算机可读存储介质 |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
GB9518800D0 (en) * | 1995-09-14 | 1995-11-15 | Gkn Westland Helicopters Ltd | Method & apparatus for determining the airspeed of rotary wing aircraft |
US9272778B2 (en) * | 2009-06-04 | 2016-03-01 | Airbus Helicopters | Device for assisting in piloting hybrid helicopter, hybrid helicopter provided with such device, and method implemented by such device |
DE102016119152B4 (de) * | 2016-10-07 | 2018-12-27 | Deutsches Zentrum für Luft- und Raumfahrt e.V. | Windmessung mittels eines Multikopters |
-
2020
- 2020-10-22 CN CN202011137516.2A patent/CN112269031B/zh active Active
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP2390670A2 (en) * | 2010-05-27 | 2011-11-30 | Honeywell International Inc. | Wind estimation for an unmanned aerial vehicle |
CN102607639A (zh) * | 2012-02-24 | 2012-07-25 | 南京航空航天大学 | 基于bp神经网络的大攻角飞行状态下大气数据测量方法 |
CN106844887A (zh) * | 2016-12-29 | 2017-06-13 | 深圳市道通智能航空技术有限公司 | 旋翼无人机的动力学建模方法及装置 |
CN106885918A (zh) * | 2017-02-10 | 2017-06-23 | 南京航空航天大学 | 一种面向多旋翼飞行器的多信息融合实时风速估计方法 |
WO2019071327A1 (en) * | 2017-10-11 | 2019-04-18 | Embraer S.A. | NEURONAL NETWORK SYSTEM HAVING TRAINING BASED ON A COMBINATION OF MODEL AND FLIGHT INFORMATION FOR ESTIMATING AIRCRAFT AIR DATA |
CN111758034A (zh) * | 2019-05-31 | 2020-10-09 | 深圳市大疆创新科技有限公司 | 风速确定方法、系统、飞行器及计算机可读存储介质 |
CN110726851A (zh) * | 2019-12-02 | 2020-01-24 | 南京森林警察学院 | 一种利用旋翼无人机测算风速的方法 |
CN111176263A (zh) * | 2020-01-23 | 2020-05-19 | 北京航天自动控制研究所 | 一种基于bp神经网络的飞行器推力故障在线辨识方法 |
Non-Patent Citations (4)
Title |
---|
《A wind estimation method with an unmanned rotorcraft for environmental monitoring tasks》;Wang J Y等;《Sensors》;20181231;第18卷(第12期);第4504-4510页 * |
《Chemical source searching by controlling a wheeled mobile robot to follow an online planned route in outdoor field environments》;Li J G等;《Sensors》;20190228;第19卷(第2期);第426-429页 * |
《Real-time wind estimation on a micro unmanned aerial vehicle using its inertial measurement unit》;Neumann P P等;《Sensors and Actuators A: Physical》;20151231(第235期);第300-310页 * |
《基于四旋翼无人机风矢量估计》;宋尧;《中国优秀硕士学位论文全文数据库 工程科技II辑》;20190515(第4期);第C031-114页 * |
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