A Technical Framework for Selection of Autonomous UAV Navigation Technologies and Sensors

The autonomous navigation of an Unmanned Aerial Vehicle(UAV)relies heavily on the navigation sensors.The UAV’s level of autonomy depends upon the various navigation systems,such as state measurement,mapping,and obstacle avoidance.Selecting the correct components is a critical part of the design process.However,this can be a particularly difficult task,especially for novices as there are several technologies and components available on the market,each with their own individual advantages and disadvantages.For example,satellite-based navigation components should be avoided when designing indoor UAVs.Incorporating them in the design brings no added value to the final product and will simply lead to increased cost and power consumption.Another issue is the number of vendors on the market,each trying to sell their hardware solutions which often incorporate similar technologies.The aim of this paper is to serve as a guide,proposing various methods to support the selection of fit-for-purpose technologies and components whilst avoiding system layout conflicts.The paper presents a study of the various navigation technologies and supports engineers in the selection of specific hardware solutions based on given requirements.The selection methods are based on easy-to-follow flow charts.A comparison of the various hardware components specifications is also included as part of this work.

Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method

Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.

Review on bio-inspired polarized skylight navigation

In this review, the research progress of bio-inspired polarized skylight navigation is evaluated from the perspectives of theoretical basis, information detection, sensor design, and navigation realization. First, the theory for characterizing the polarization mode of the skylight was introduced. Second, using sunlight, moonlight, and ocean as backgrounds, the measurement results of skylight polarization distribution under different weather conditions are described to compare the variation patterns. Third, the development history and research outcomes of bionic polarization navigation sensor for polarized skylight detection and navigation information calculation are categorized into two types, namely non-imaging and imaging types. In precision measurement, the non-imaging type is higher than the imaging type, and the accuracy that it can reach is ± 0.1° of navigation accuracy without drift error. Fourth, two polarized skylight orientation algorithms,E-vector-based method and Solar Meridian-Anti Solar Meridian(SM-ASM)-based method are summarized. Fifth, this review details the combined application of polarized skylight navigation sensors and Inertial Navigation System(INS), Global Navigation Satellite System(GNSS), Vision,Simultaneous Localization and Mapping(SLAM), and other navigation systems. The yaw and trajectory accuracy can be increased by about 40% compared to classical navigation system in complex outdoor environments. Finally, the future development trends of polarization navigation are presented.