scholarly journals Sensor Fusion Based Fault-Tolerant Attitude Estimation Solutions for Small Unmanned Aerial Vehicles

2011 ◽  
Author(s):  
Jason Nicholas Gross
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Sensor Fusion ◽  
Fault Tolerant ◽  
Attitude Estimation ◽  
Aerial Vehicles

scholarly journals Attitude estimation using horizon detection in thermal images

2018 ◽  
Vol 10 (4) ◽  
pp. 352-361 ◽  
Author(s):  
Adrian Carrio ◽  
Hriday Bavle ◽  
Pascual Campoy
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Infinite Horizon ◽  
Attitude Estimation ◽  
Thermal Image ◽  
Image Sensors ◽  
Image Resolution ◽  
Estimation Methods ◽  
Operation Conditions ◽  
Aerial Vehicles ◽  
Horizon Detection

The lack of redundant attitude sensors represents a considerable yet common vulnerability in many low-cost unmanned aerial vehicles. In addition to the use of attitude sensors, exploiting the horizon as a visual reference for attitude control is part of human pilots’ training. For this reason, and given the desirable properties of image sensors, quite a lot of research has been conducted proposing the use of vision sensors for horizon detection in order to obtain redundant attitude estimation onboard unmanned aerial vehicles. However, atmospheric and illumination conditions may hinder the operability of visible light image sensors, or even make their use impractical, such as during the night. Thermal infrared image sensors have a much wider range of operation conditions and their price has greatly decreased during the last years, becoming an alternative to visible spectrum sensors in certain operation scenarios. In this paper, two attitude estimation methods are proposed. The first method consists of a novel approach to estimate the line that best fits the horizon in a thermal image. The resulting line is then used to estimate the pitch and roll angles using an infinite horizon line model. The second method uses deep learning to predict attitude angles using raw pixel intensities from a thermal image. For this, a novel Convolutional Neural Network architecture has been trained using measurements from an inertial navigation system. Both methods presented are proven to be valid for redundant attitude estimation, providing RMS errors below 1.7° and running at up to 48 Hz, depending on the chosen method, the input image resolution and the available computational capabilities.

Fault-Tolerant Formation Control of Unmanned Aerial Vehicles in the Presence of Actuator Faults and Obstacles

2016 ◽  
Vol 04 (03) ◽  
pp. 197-211 ◽  
Author(s):  
Zhixiang Liu ◽  
Chi Yuan ◽  
Xiang Yu ◽  
Youmin Zhang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Formation Control ◽  
Active Fault ◽  
Fault Tolerant ◽  
Actuator Faults ◽  
Outer Loop ◽  
Actuator Failures ◽  
Aerial Vehicles ◽  
Control Scheme ◽  
Multiple Unmanned Aerial Vehicles

This paper presents a leader-follower type of fault-tolerant formation control (FTFC) methodology with application to multiple unmanned aerial vehicles (UAVs) in the presence of actuator failures and potential collisions. The proposed FTFC scheme consists of both outer-loop and inner-loop controllers. First, a leader-follower control scheme with integration of a collision avoidance mechanism is designed as the outer-loop controller for guaranteeing UAVs to keep the desired formation while avoiding the approaching obstacles. Then, an active fault-tolerant control (FTC) strategy for counteracting the actuator failures and also for preventing the healthy actuators from saturation is synthesized as the inner-loop controller. Finally, a group of numerical simulations are carried out to verify the effectiveness of the proposed approach.

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