Diagnosis of Fault Modes Masked by Control Loops with an Application to Autonomous Hovercraft Systems

This paper introduces a methodology for the design, testing and assessment of incipient failure detection techniques for failing components/systems of an autonomous vehicle masked or hidden by feedback control loops. It is recognized that the optimum operation of critical assets (aircraft, autonomous systems, etc.) may be compromised by feedback control loops by masking severe fault modes while compensating for typical disturbances. Detrimental consequences of such occurrences include the inability to detect expeditiously and accurately incipient failures, loss of control and inefficient operation of assets in the form of fuel overconsumption and adverse environmental impact. We pursue a systems engineering process to design, construct and test an autonomous hovercraft instrumented appropriately for improved autonomy. Hidden fault modes are detected with performance guarantees by invoking a Bayesian estimation approach called particle filtering. Simulation and experimental studies are employed to demonstrate the efficacy of the proposed methods

Particle filter-based architecture for video target tracking and geo-location using multiple UAVs

Research in the areas of target detection, tracking, and geo-location is most important for enabling an unmanned aerial vehicle (UAV) platform to autonomously execute a mission or task without the need for a pilot or operator. Small-class UAVs and video camera sensors complemented with "soft sensors" realized only in software as a combination of a priori knowledge and sensor measurements are called upon to replace the cumbersome precision sensors on-board a large class UAV. The objective of this research is to develop a geo-location solution for use on-board multiple UAVs with mounted video camera sensors only to accurately geo-locate and track a target. This research introduces an estimation solution that combines the power of the particle filter with the utility of the video sensor as a general solution for passive target geo-location on-board multiple UAVs. The particle filter is taken advantage of, with its ability to use all of the available information about the system model, system uncertainty, and the sensor uncertainty to approximate the statistical likelihood of the target state. The geo-location particle filter is tested online and in real-time in a simulation environment involving multiple UAVs with video cameras and a maneuvering ground vehicle as a target. Simulation results show the geo-location particle filter estimates the target location with a high accuracy, the addition of UAVs or particles to the system improves the location estimation accuracy with minimal addition of processing time, and UAV control and trajectory generation algorithms restrict each UAV to a desired range to minimize error.PhDCommittee Chair: Vachtsevanos, George; Committee Member: Johnson, Eric; Committee Member: Michaels, Thomas; Committee Member: Romberg, Justin; Committee Member: Yezzi, Anthon