Design and integration of vision based sensors for unmanned aerial vehicles navigation and guidance

Abstract

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.

A Low-cost Vision Based Navigation System for Small Size Unmanned Aerial Vehicle Applications

“…Equations (22) and (23) are in form known as line plane correspondence problem as proposed by [18]. Recalling the equations for a projective perspective camera:…”

“…The corresponding VIG and VIGA integrated navigation modes were simulated using MATLAB TM covering all relevant flight phases of the AEROSONDE UAV (straight climb, straight-and-level flight, straight turning, turning descend/ climb, straight descent, etc.). The navigation system outputs were fed to a hybrid Fuzzy-logic/PID controller designed at Cranfield University for the AEROSONDE UAV and capable of operating with stand-alone VBN, as well as with VIG/VIGA and other sensors data [23,24]. …”

Carrier-phase GNSS Attitude Determination and Control for Small Unmanned Aerial Vehicle Applications

“…A A (12) Although the system Eq. (12) has a unique solution for  in a real system it is necessary to take into account the possible errors in the determination of the values of Ŝ n and β n .…”

“…Due to the low volume/ weight of current carrier-phase GNSS receivers, and the extremely high accuracy attainable notwithstanding their lower cost, interferometric GNSS technology is becoming an excellent candidate for future UAV applications [10]. The accuracy of the GNSS Attitude Determination (GAD) systems is affected by several factors including the selected equipment/algorithms and the specific platform installation geometry, with the baseline length and multipath errors being the key elements dominating GAD systems performance [10][11][12] developed an extension of the known Least-squares Ambiguity Decorrelation Adjustment (LAMBDA) method [13] for solving nonlinearly constrained ambiguity resolution problems associated to GNSS attitude determination.…”

Low-Cost Navigation and Guidance Systems for Unmanned Aerial Vehicles — Part 1: Vision-Based and Integrated Sensors

“…In our research, INS-MEMS errors are modeled as White Noise (WN) or as Gauss-Markov (GM) processes [10,25]. The error parameters in [18] were considered for our simulation.…”