Cooperative Virtual Sensor for Fault Detection and Identification in Multi-UAV Applications

Abstract

This paper considers the problem of fault detection and identification (FDI) in applications carried out by a group of unmanned aerial vehicles (UAVs) with visual cameras. In many cases, the UAVs have cameras mounted onboard for other applications, and these cameras can be used as bearing-only sensors to estimate the relative orientation of another UAV. The idea is to exploit the redundant information provided by these sensors onboard each of the UAVs to increase safety and reliability, detecting faults on UAV internal sensors that cannot be detected by the UAVs themselves. Fault detection is based on the generation of residuals which compare the expected position of a UAV, considered as target, with the measurements taken by one or more UAVs acting as observers that are tracking the target UAV with their cameras. Depending on the available number of observers and the way they are used, a set of strategies and policies for fault detection are defined. When the target UAV is being visually tracked by two or more observers, it is possible to obtain an estimation of its 3D position that could replace damaged sensors. Accuracy and reliability of this vision-based cooperative virtual sensor (CVS) have been evaluated experimentally in a multivehicle indoor testbed with quadrotors, injecting faults on data to validate the proposed fault detection methods.

Selected Aging Effects in Triaxial MEMS Accelerometers

“…Many different calibration procedures have been successfully used so far, some typical, as, e.g., in [43], some quite sophisticated, as, e.g., in [8-10, 44, 45]. Another interesting approach to overcoming the aging effects may be the application of additional sensors, e.g., visual camera, which may be also useful for identification of other failures of a device [46].…”

Automated Coregistration of Multisensor Orthophotos Generated from Unmanned Aerial Vehicle Platforms

“…Unmanned aerial vehicles (UAVs) have now become competitive platforms for remote sensing-based applications as they can be easily and cost-effectively deployed while collecting geospatial data with higher temporal/spatial resolution than other platforms [1][2][3][4][5][6][7][8][9][10]. They can also collect multisensor images by mounting diverse sensors on the UAV platforms to achieve a wide range of remote sensing applications [11][12][13][14]. To perform efficient geospatial analysis using the multisensor images acquired from a UAV platform, image coregistration, which geometrically overlays the images, is an essential step [14,15].…”

“…They can also collect multisensor images by mounting diverse sensors on the UAV platforms to achieve a wide range of remote sensing applications [11][12][13][14]. To perform efficient geospatial analysis using the multisensor images acquired from a UAV platform, image coregistration, which geometrically overlays the images, is an essential step [14,15]. The most stable and accurate method to achieve coregistration is the installation of air-photo targets in a study area that coincides with UAV flight.…”

UAV Sensor Fault Detection Using a Classifier without Negative Samples: A Local Density Regulated Optimization Algorithm

“…The flight control system, which is responsible for the accomplishment of flight tasks, is the core of the UAV. Through comparing the actual state of the UAV indicated by the sensor data with the desired state, it sends commands to actuators to make the actual state follow the desired state, completing the closed-loop control process [ 3 , 4 ]. Sensors provide sensing data for the flight control system to make analysis and generate control commands, and faulty sensor data will cause erroneous commands and undesirable consequences.…”