Vehicle Dynamic Model Based Navigation for Small UAVs

The dominant navigation system for small civilian UAVs today is based on integration of inertial navigation system (INS) and global navigation satellite system (GNSS). This strategy works well to navigate the UAV, as long as proper reception of GNSS signal is maintained. However, when GNSS outage occurs, the INS-based navigation solution drifts very quickly, considering the limited quality of IMU(s) employed in INS for small UAVs. In beyond visual line of sight (BVLOS) flights, this poses the serious danger of losing the UAV and its eventual falling down. Limited payload capacity and cost for small UAVs, as well as the need for operating in different conditions, with limited visibility for example, make it challenging to find a solution to reach higher levels of navigation autonomy based on conventional approaches. This thesis aims to improve the accuracy of autonomous navigation for small UAVs by at least one order of magnitude. The proposed novel approach employs vehicle dynamic model (VDM) as process model within navigation system, and treats data from other sensors such as IMU, barometric altimeter, and GNSS receiver, whenever available, as observations within the system. Such improvement comes with extra effort required to determine the VDM parameters for any specific UAV. This work investigates the internal capability of the proposed system for estimating VDM parameters as part of the augmented state vector within an extended Kalman filter (EKF) as the estimator. This reduces the efforts required to setup such navigation system that is platform dependent. Multiple experimental flights using two custom made fixed-wing UAVs are presented together with Monte-Carlo simulations. The results reveal improvements of 1 to 2 orders of magnitude in navigation accuracy during GNSS outages of a few minutes' duration. Computational cost for the proposed VDM-based navigation does not exceed 3~times that of conventional INS-based systems, which establishes its applicability for online application. A global sensitivity analysis is presented, spotting the VDM parameters with higher influence on navigation performance. This provides insight for design of calibration procedures. The proposed VDM-based navigation system can be interesting for professional UAVs from at least two points of view. Firstly, it adds little to no extra hardware and cost to the UAV. Secondly and more importantly, it might be currently the only way to reach such significant improvement in navigation autonomy for small UAVs regardless of visibility conditions and electromagnetic signals reception. Possibly, such environmental condition independence for navigation system may be needed to obtain certifications from legal authorities to expand UAV applications to new types of mission.

Assessment of VDM-based autonomous navigation of a UAV under operational conditions

Mehran Khaghani, Jan Skaloud

This paper presents extensions and practical realization of a previously proposed novel approach to navigation and sensor integration for small unmanned aerial vehicles (UAV). The proposed approach employs vehicle dynamic model (VDM) as process model within navigation system, and treats data from other sensors such as inertial measurement unit (IMU), barometric altimeter, and global navigation satellite system (GNSS) receiver as observations within the system. In comparison to conventional approach that employs inertial navigation system (INS) as process model, employing VDM requires no added hardware, yet significantly improves navigation performance, especially in case of GNSS outages. Experimental results from a real flight on a custom made fixed-wing UAV, as well as Monte Carlo simulation results, reveal improvements of 1 to 2 orders of magnitude in navigation accuracy during GNSS outages of 3 minutes’ duration. This is a prerequisite for safer navigation without exteroceptive sensors. Uncertainty levels are predicted consistently within the filter, and a discussion on observability based on covariance matrix analysis is presented. Computation time is also compared to conventional INS-based approach.

2018

Evaluation of Wind Effects on UAV Autonomous Navigation Based on Vehicle Dynamic Model

Mehran Khaghani, Jan Skaloud

A novel approach to autonomous navigation for small UAVs was recently introduced by the authors. An abridged description of this approach can be found in present paper, with its main contribution being further evaluation of effects of wind on navigation performance. The vehicle dynamic model (VDM) serves as the main process model within the introduced navigation filter. The proposed method exploits the knowledge of physical properties of the UAV together with control input with the goal to significantly increase the accuracy and reliability of autonomous navigation. This is especially relevant for small UAVs with low-cost IMUs on-board and no extra sensor added to the conventional INS/GNSS setup. The improvement is of special interest in case of GNSS outages, where inertial coasting drifts very quickly. In the proposed architecture, the solution to VDM equations provides the estimate of position, velocity, and attitude, which is updated within the navigation filter based on available observations, such as IMU data or GNSS measurements. The filter is capable of estimating wind velocity and dynamic model parameters, in addition to navigation states and IMU sensor errors. Robustness and scalability of the navigation system against random changes in wind velocity are investigated via Monte Carlo simulations using real 3D wind velocity data. In case of GNSS outages of a few minutes, position and attitude accuracy experiences improvements of orders of magnitude compared to conventional kinematic modeling in INS/GNSS integration. Simulations also reveal that navigation errors are almost doubled as wind velocity doubles, which gives an estimation of the scalability of the navigation system.

Inst Navigation2016

Optimal Stochastic Sensor Error Modeling based on Actual Impact on Quality of GNSS-INS Integrated Navigation

Stéphane Guerrier, Mehran Khaghani, Jan Skaloud

Proper modeling of stochastic errors in inertial sensors plays a crucial role in the achievable quality of GNSS-INS integration especially with low-cost inertial sensors. Generalized Method of Wavelet Moments (GMWM) can model the underlying process for such errors with arbitrarily complex structure to obtain close match to the observed errors in terms of wavelet variance. In comparison to the widely used and IEEE adopted error modeling using Allan Variance, this method provides consistent estimation, ability to estimate parameters of composite stochastic models of much higher complexities, and considerably easier usage. However, the level of improvement in navigation quality does not necessarily grow proportionally to the fidelity of the error models. Therefore, opting for unnecessarily complex models may only increase computational load with no tangible gain in navigation quality. On the other hand, converging estimation of higher complexity models, generally speaking, requires longer estimation periods and higher dynamics to improve observability of error states. This implies yet another motivation to find an optimal sensor error model avoiding unnecessary complexities. In this paper, we employ two methods to investigate the effect of model complexity on integrated navigation performance. Firstly, a covariance propagation is performed on static conditions, as is the standard scenario in error analysis of inertial navigation systems. Afterwards, an emulation study is performed based on a real Unmanned Aerial Vehicle (UAV) flight and error signals of a Navchip V2 Inertial Measurement Unit (IMU). Results of both methods are in general agreement, and suggest that more complex models in general provide higher accuracy for the navigation system and a more consistent covariance prediction by the navigation filter. This difference is, however, more noticable only in GNSS outages of longer duration (tens of seconds). However, the benefits of more complex models may be only marginal in other applications, depending on the duration of inertial coasting and availability of other sensory data such as GNSS observations.

2019

Optimal Stochastic Sensor Error Modeling based on Actual Impact on Quality of GNSS-INS Integrated Navigation

Stéphane Guerrier, Mehran Khaghani, Jan Skaloud

2019