Model-Based Autonomous Navigation with Moment of Inertia Estimation for Unmanned Aerial Vehicles

Abstract

The dominant navigation system for low-cost, mass-market Unmanned Aerial Vehicles (UAVs) is based on an Inertial Navigation System (INS) coupled with a Global Navigation Satellite System (GNSS). However, problems tend to arise during periods of GNSS outage where the navigation solution degrades rapidly. Therefore, this paper details a model-based integration approach for fixed wing UAVs, using the Vehicle Dynamics Model (VDM) as the main process model aided by low-cost Micro-Electro-Mechanical Systems (MEMS) inertial sensors and GNSS measurements with moment of inertia calibration using an Unscented Kalman Filter (UKF). Results show that the position error does not exceed 14.5 m in all directions after 140 s of GNSS outage. Roll and pitch errors are bounded to 0.06 degrees and the error in yaw grows slowly to 0.65 degrees after 140 s of GNSS outage. The filter is able to estimate model parameters and even the moment of inertia terms even with significant coupling between them. Pitch and yaw moment coefficient terms present significant cross coupling while roll moment terms seem to be decorrelated from all of the other terms, whilst more dynamic manoeuvres could help to improve the overall observability of the parameters.

Development of Response Surface Model of Endurance Time and Structural Parameter Optimization for a Tailsitter UAV

“…The three-dimensional model of the UAV was built by computer aided tri-dimensional interface application (CATIA), which is widely used in aircraft design [37]. Owing to the symmetrical layout of the UAV, a three-dimensional model was built for the left side of the fuselage, and the structures of the propeller, motor, and motor seat were simplified.…”

A Model-based Tightly Coupled Architecture for Low-Cost Unmanned Aerial Vehicles for Real-Time Applications

“…This is essential because it would allow changing some aspects of the aircraft such as the payload or the propeller without a new system identification routine. The capability for online parameter calibration is discussed briefly in Section IV and the interested reader is directed to [11] for an in-depth analysis for a loosely coupled scheme. It is important to note that we do not aim to present a complete solution to the parameter estimation problem but rather show the navigation performance achieved with some parameter uncertainty.…”

“…More recently, some authors have explored the use of a vehicle dynamic model (VDM) as either the main process model in a model-based approach [9]- [11] or as an aiding tool in a model-aided approach [12]- [14]. The use of a VDM has gained research popularity because it can give significant performance improvements without adding extra weight to the overall system and can meet the stringent cost and power requirements, inherent in low-cost UAV applications [11].…”

“…The approach avoids duplicate states and showed significant performance improvements, even in the presence of VDM parameter errors. Khaghani and Skaloud [10], and Mwenegoha et al [11] extended the model-based approach proposed by Sendobry to fixed-wing UAVs. Simulation results revealed two orders of magnitude improvement in position estimation during extended GNSS outages.…”

“…The vast majority of VDM integration schemes in the literature rely on using filtered GNSS measurements output by a GNSS receiver to provide a bounded navigation solution. Filtered measurements from a GNSS receiver are usually not available during a GNSS outage or when tracking less than four satellites, which can cause the navigation solution to drift even when using a VDM [9]- [11], [13], [16]. Further, model-aided INS schemes can easily be disabled in case of IMU failure and additionally, multi-process model schemes introduce duplicate states that increase computational cost [17].…”