The increasing interest in low-altitude unmanned aerial vehi- cle (UAV) operations is bringing along safety concerns. Per- formance of small, low-cost UAVs drastically changes with type, size and controller of the vehicle. Their reliability is sig- nificantly lower when compared to reliability of commercial aircraft, and the availability of on-board sensors for health and state awareness is extremely limited due to their size and propulsion capabilities. Uncertainty plays a dominant role in such a scenario, where a variety of UAVs of differ- ent size, propulsion systems, dynamic performance and reli- ability enters the low-altitude airspace. Unexpected failures could have dangerous consequences for both equipment and humans within that same airspace. As a result, a number of research tasks and methodologies are being proposed in the area of UAV dynamic modeling, health and safety monitor- ing, but uncertainty quantification is rarely addressed. Thus, this paper proposes a perspective towards uncertainty quan- tification for autonomous systems, giving special emphasis to UAV health monitoring application. A formal approach to classify uncertainty is presented; it is utilized to identify the uncertainty sources in UAVs health and operations, and then map uncertainty within a predictive process. To show the application of the methodology proposed here, the design of a model-based powertrain health monitoring algorithm for small-size UAVs is presented as case study. The example il- lustrates how the uncertainty quantification approach can help the modeling strategy, as well as the assessment of diagnostic and prognostic performance.
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uav, powertrain, uncertainty quantification, battery, motor, ESC
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