不同类型无人机自主导航捷联惯导系统特性优化及外部校正传感器

E. I. Starovoytov
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摘要

目前,无人机可以用于地形工程、扩展工程结构的状态监测和诊断、向难以到达的地方运送货物等。为了使无人机得到更广泛的应用,解决更多的任务,需要提高无人机在导航支持方面的自主程度。用于自主导航的无人机控制系统采用基于各种陀螺仪的捷联惯性导航系统。基于激光陀螺仪的捷联惯导系统质量大,精度高。载荷质量与导航设备质量可通约的无人机需要对捷联惯导系统特性进行优化。针对激光陀螺捷联惯导系统的质量和精度,提出了一种优化方法。对不同载荷质量的捷联惯导系统和无人机载体的特性进行了综合评估。在卫星导航不可用的情况下,考虑了各种SINS校正方法。对于陆上飞行,可以使用相关极端导航系统(CENS)和SLAM方法(用于同时定位和绘图)。cns需要参考地形描述和足够密度的地标。在基于SLAM算法的导航中,不需要参考地形描述,在适当的大气路径条件下,通过光学传感器即可获得初始数据。无论大气路径的条件、下垫面类型及其详细信息如何,无人机坐标都可以通过使用多普勒系统(DISS)的多普勒航位推算确定。在低空和中高空,SINS校正是可能的,只需要航向传感器数据来计算路径角。在与DISS和3D Flash雷达传感器(用于实现SLAM算法)相结合时,使用基于光纤陀螺仪的低精度捷联惯导系统比基于激光陀螺仪的系统更理想。所得结果可用于开发中型、轻型和重型中型无人机的导航系统。
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Optimizing Strap-down Inertial Navigation Systems Characteristics and External Correction Sensors for Autonomous Navigation of Different UAV Classes
Currently, unmanned aerial vehicles (UAVs) can be used in topographic works, condition monitoring and diagnostics of extended engineering structures, delivering goods to hard-to-reach places, etc. To provide the widespread UAVs applications and raise the number of tasks to be solved through their using, it is necessary to increase their autonomy degree in terms of navigation support, in particular. Unmanned aerial vehicles (UAV) control systems for autonomous navigation use the strap-down inertial navigation systems (SINS) based on various types of gyroscopes. SINS based on the laser gyroscopes, which have a large mass, have the best accuracy. UAVs with a payload mass that is commensurable with the mass of navigation equipment require optimization of SINS characteristics. An optimization method has been developed to enable obtaining a Pareto set for the mass and accuracy of SINS based on laser gyroscopes. A comprehensive assessment of the characteristics of SINS and UAV carrier with different payload mass has been performed. Various SINS correction methods are considered when satellite navigation is unavailable.For overland flights, the correlation-extreme navigation systems (CENS) and SLAM methods (for simultaneous localisation and mapping) can be used. CENS require a reference lay-of-the-land description and a sufficient density of landmarks. In navigation based on SLAM algorithms, there is no need in the reference lay-of-the-land description, and the initial data can be obtained through the optical sensors under appropriate condition of the atmospheric path.Regardless of the condition of the atmospheric path, type of the underlying surface and its information available in detail, the UAV coordinates can be determined by Doppler dead reckoning using a Doppler system (DISS). At low and medium altitudes SINS correction is possible, only heading sensor data are needed to calculate the path angle.In combining with DISS and 3D Flash Ladar sensors (for implementing SLAM algorithms), it is more optimal to use low-accuracy SINS based on fibre-optic gyroscopes rather than laser gyro-based systems.The results obtained can be used when developing navigation systems for medium, light and heavy-medium UAVs.
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