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Vision Based Sensors and Multisensor Systems for Unmanned Aerial Vehicles Navigation and Guidance

机译:基于视觉的无人航空车辆导航和指导的传感器和多传感器系统

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In this paper we present a novel Navigation and Guidance System (NGS) for Unmanned Aerial Vehicles (UAVs) based on Vision Based Navigation (VBN) and other avionics sensors. The main objective of our research is to design a lowcost and low-weight/volume NGS capable of providing the required level of performance in all flight phases of modern small- to medium-size UAVs, with a special focus on automated precision approach and landing, where VBN techniques can be fully exploited in a multisensory integrated architecture. Various existing techniques for VBN are compared and the Appearance-based Navigation (ABN) approach is selected for implementation. Feature extraction and optical flow techniques are employed to estimate flight parameters such as roll angle, pitch angle, deviation from the runway and body rates. Additionally, we address the possible synergies between VBN, Global Navigation Satellite System (GNSS) and MEMS-IMU (Micro-Electromechanical System Inertial Measurement Unit) sensors and also the use of Aircraft Dynamics Models (ADMs) to provide additional information suitable to compensate for the shortcomings of VBN sensors in high-dynamics attitude determination tasks. An Extended Kalman Filter (EKF) is developed to fuse the information provided by the different sensors and to provide estimates of position, velocity and attitude of the platform in real-time. Two different integrated navigation system architectures are implemented. The first uses VBN at 20 Hz and GPS at 1 Hz to augment the MEMS-IMU running at 100 Hz. The second mode also includes the ADM (computations performed at 100 Hz) to provide augmentation of the attitude channel. Simulation of these two modes is performed in a significant portion of the Aerosonde UAV operational flight envelope and performing a variety of representative manoeuvres (i.e., straight climb, level turning, turning descent and climb, straight descent, etc.). Simulation of the first integrated navigation system architecture (VBN/GPS/IMU) shows that the integrated system can reach position, velocity and attitude accuracies compatible with CAT-II precision approach requirements. Simulation of the second system architecture (VBN/GPS/IMU/ADM) shows promising results since the achieved attitude accuracy is higher using the ADM/VBS/IMU than using VBS/IMU only. However, due to rapid divergence of the ADM virtual sensor, there is a need for a frequent re-initialisation of the ADM data module, which is strongly dependent on the UAV flight dynamics and the specific manoeuvring transitions performed. Finally, the output provided by the VBN and integrated navigation sensor systems is used to design a flight control system using a hybrid Fuzzy Logic and Proportional-Integral-Derivative (PID) controller for the Aerosonde UAV.
机译:在本文中,我们为基于视觉的导航(VBN)和其他航空电子传感器提供了一种新的导航和指导系统(NGS),用于无人驾驶飞行器(UAV)和其他航空电子传感器。我们研究的主要目的是设计一种能够在现代小于中型无人机的所有飞行阶段提供所需的性能水平,专注于自动化精密近视和着陆的低重量/卷NG。 ,其中VBN技术可以在多群体集成架构中完全利用。比较了VBN的各种现有技术,并选择了基于外观的导航(ABN)方法进行实现。特征提取和光学流动技术用于估计诸如辊角,俯仰角的飞行参数,与跑道和体速率的偏差。此外,我们解决了VBN,全球导航卫星系统(GNSS)和MEMS-IMU(微机电系统惯性测量单元)传感器的可能协同作用以及飞机动力学模型(ADMS)的使用,以提供适合补偿的附加信息VBN传感器在高动态姿态确定任务中的缺点。开发了一个扩展的卡尔曼滤波器(EKF)以使不同传感器提供的信息熔断并实时地提供平台的位置,速度和姿态的估计。实现了两个不同的集成导航系统架构。首先使用20 Hz和GPS的VBN,1 Hz,以增强以100 Hz运行的MEMS-IMU。第二模式还包括ADM(在100Hz执行的计算)以提供姿态信道的增强。这些两种模式的模拟是在Aerosonde UAV运营飞行包的重要部分中进行的,并且执行各种代表演习(即,直爬,水平转动,转动下降和爬升,直下降等)。第一综合导航系统架构(VBN / GPS / IMU)的仿真表明,集成系统可以达到与CAT-II精密方法要求兼容的位置,速度和姿态精度。第二系统架构(VBN / GPS / IMU / ADM)的仿真显示了有希望的结果,因为使用ADM / VBS / IMU的实现姿态精度比仅使用VBS / IMU更高。然而,由于ADM虚拟传感器的快速分歧,需要频繁地重新初始化ADM数据模块,这强烈依赖于UAV飞行动态和执行的特定机动转换。最后,通过提供VBN和集成导航传感器系统的输出被用于设计使用用于Aerosonde UAV混合模糊逻辑和比例 - 积分 - 微分(PID)控制器的飞行控制系统。

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