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.
Highlights
Reliability and fault tolerance have always been an important issue in unmanned aerial vehicles (UAVs) [1], where fault detection and identification (FDI) techniques play an important role in the efforts to increase the reliability of the systems [2]
Most FDI techniques for single UAVs that appear in the literature use model-based methods, which try to diagnose faults using the redundancy of some mathematical description of the system dynamics and sensors onboard the UAV
This paper describes the development of a FDI system that integrates the sensors available in a multi-UAV fleet in order to detect faults in the sensors of one of its member UAV
Summary
Reliability and fault tolerance have always been an important issue in UAVs [1], where fault detection and identification (FDI) techniques play an important role in the efforts to increase the reliability of the systems [2] It is even more important when teams of aerial vehicles cooperate closely between them and the environment; such is the case in multi-UAV missions and formation flight. What has not been thoroughly explored is the use of the sensors onboard the other vehicles of the team for detection of faults in an autonomous vehicle, which requires sensing the state of a vehicle from the other team components This scheme requires the computation of the relative position of a UAV from another UAV. The vision-based FDI system, which may be useful are mentioned, identifying typical faults on UAVs and proposing several strategies and methods for their detection and identification.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
