A navigation system for precision lunar descent and landing is presented and analyzed. The navigation algorithm is based upon the extended Kalman Filter and employs measurements from an inertial measurement unit to propagate the vehicle position, velocity, and attitude forward in time. External measurements from an altimeter, star camera, terrain camera, and velocimeter are utilized in state estimate updates. The navigation algorithm also attempts to estimate the values of uncertain parameters associated with the sensors. The navigation algorithm also estimates the map-tie angle of the landing site which is a measure of the misalignment of the actual landing site location on the surface of the Moon versus the estimated position of the landing site. The navigation algorithm is subject to a sensitivity analysis which investigates the contribution of each error source to the total estimation performance of the navigation system. Per the results of the sensitivity analysis, it is found that certain error sources need not be actively estimated to achieve similar estimation performance at a reduced computational burden. A new, reduced-order system is presented and tested through covariance analysis and a monte carlo analysis. The new system is shown to have comparable estimation performance at a fraction of the computer run-time, making it more suitable for a real-time implementation. / text

The concept driver distraction is diffuse and no clear definition exists, which causes troubles when it comes to driver attention monitoring. This thesis takes an approach where eyetracking data from experienced drivers along with radar data has been used and analysed in an attempt to set up adaptive rules of how and how often the driver needs to attend to different objects in its surroundings, which circumvents the issue of not having a clear definition of driver distraction. In order to do this, a target tracking algorithm has been implemented that refines the output from the radar, subsequently used together with the eye-tracking data to in a statistical manner, in the long term, try to answer the question for how long is the driver allowed to look away in different driving scenarios? The thesis presents a proof of concept of this approach, and the results look promising.

Condition monitoring of hydraulic systems is an area that has grown substantially in the last few decades. This thesis presents a scheme that automatically generates the fault symptoms by on-line processing of raw sensor data from a real test rig. The main purposes of implementing condition monitoring in hydraulic systems are to increase productivity, decrease maintenance costs and increase safety. Since such systems are widely used in industry and becoming more complex in function, reliability of the systems must be supported by an efficient monitoring and maintenance scheme. This work proposes an accurate state space model together with a novel model-based fault diagnosis methodology. The test rig has been fabricated in the Process Automation and Robotics Laboratory at UBC. First, a state space model of the system is derived. The parameters of the model are obtained through either experiments or direct measurements and manufacturer specifications. To validate the model, the simulated and measured states are compared. The results show that under normal operating conditions the simulation program and real system produce similar state trajectories. For the validated model, a condition monitoring scheme based on the Unscented Kalman Filter (UKF) is developed. In simulations, both measurement and process noises are considered. The results show that the algorithm estimates the iii system states with acceptable residual errors. Therefore, the structure is verified to be employed as the fault diagnosis scheme. Five types of faults are investigated in this thesis: loss of load, dynamic friction load, the internal leakage between the two hydraulic cylinder chambers, and the external leakage at either side of the actuator. Also, for each leakage scenario, three levels of leakage are investigated in the tests. The developed UKF-based fault monitoring scheme is tested on the practical system while different fault scenarios are singly introduced to the system. A sinusoidal reference signal is used for the actuator displacement. To diagnose the occurred fault in real time, three criteria, namely residual moving average of the errors, chamber pressures, and actuator characteristics, are considered. Based on the presented experimental results and discussions, the proposed scheme can accurately diagnose the occurred faults.

In this thesis we consider two combinatorial optimization problems that relate to the field of persistent monitoring. In the first part, we extend the classic problem of finding the maximum weight Hamiltonian cycle in a graph to the case where the objective is a submodular function of the edges. We consider a greedy algorithm and a 2-matching based algorithm, and we show that they have approximation factors of 1/2+κ and max{2/(3(2+κ)),(2/3)(1-κ)} respectively, where κ is the curvature of the submodular function. Both algorithms require a number of calls to the submodular function that is cubic to the number of vertices in the graph. We then present a method to solve a multi-objective optimization consisting of both additive edge costs and submodular edge rewards. We provide simulation results to empirically evaluate the performance of the algorithms. Finally, we demonstrate an application in monitoring an environment using an autonomous mobile sensor, where the sensing reward is related to the entropy reduction of a given a set of measurements. In the second part, we study the problem of selecting sensors to obtain the most accurate state estimate of a linear system. The estimator is taken to be a Kalman filter and we attempt to optimize the a posteriori error covariance. For a finite time horizon, we show that, under certain restrictive conditions, the problem can be phrased as a submodular function optimization and that a greedy approach yields a 1-1/(e^(1-1/e))-approximation. Next, for an infinite time horizon, we characterize the exact conditions for the existence of a schedule with bounded estimation error covariance. We then present a scheduling algorithm that guarantees that the error covariance will be bounded and that the error will die out exponentially for any detectable LTI system. Simulations are provided to compare the performance of the algorithm against other known techniques.

In this thesis, an algorithm that improves the performance of the extended Kalman filter (EKF) on the mobile robot localization issue is proposed, which is aided by the cooperation of neural network and fuzzy logic. An EKF is used to fuse the information acquired from both the robot optical encoders and the external sensors in order to estimate the current robot position and orientation. Then the error covariance of the EKF is tracked by the covariance matching technique. When the output of the matching technique does not meet the desired condition, a fuzzy logic is employed to adjust the error covariance matrix to modify it back to the desired value range. Since the fuzzy logic is lack of the capability of learning, a neural network is presented in the algorithm to train the EKF. The simulation results demonstrate that, with the comparison to the odometry and the standard EKF method under the same error divergence condition, the proposed extended Kalman filter effectively improves the accuracy of the localization of the mobile robot system and effectively prevents the filter divergence.

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The ability to acquire accurate information of the surrounding road environment is crucial for autonomous driving and advanced driver assistance systems. A method to estimate the shape of the road has been developed and evaluated. The estimate is based on fusion of data from a road marking detector, a radar tracker, map data, GPS, and inertial sensors. The method is intended for highway use and focus has been on increasing the availability of a sufficiently accurate road shape estimate in the event of sensor failures. To make use of past sensor measurements, an extended Kalman filter has been used together with dynamical models for the road and the ego vehicle. Results from a performance evaluation show that the road shape estimate clearly benefits from being based on a fusion of sensor data. The different sensors have also proven to be of various importance to the different parameters that describe the road shape. / Fordon som kan köra autonomt, det vill säga utan förare, är ett mål för fordonsindustrin och en dröm för många bilägare. Det skulle möjliggöra för förare att använda tiden till annat och minska personalkostnader för transportbolag. Säkerheten på våra vägar skulle även kunna förbättras eftersom att ett sådant system har möjlighet att reagera snabbare än någon människa och drabbas inte av trötthet eller störs av andra passagerare. Förmåga att kunna inhämta och tolka information om den omkringliggande trafiksituationen är ytterst nödvändigt för att kunna utveckla autonoma fordon och behövs även för mer avancerade moderna säkerhetssytem, som till exempel kollissionsvarningssystem. En viktig del i detta är att kunna uppfatta hur formen på vägen ser ut. Målet med detta examensarbete är att utveckla en algoritm som estimerar vägens form baserat på ett antal sensorer monterade på ett fordon och information från en kartdatabas. Den största vikten har legat på att algoritmen alltid ska kunna leverera en tillräckligt bra skattning, även i perioder när sensormätningar inte finns tillgängliga på grund av att sensorer fallerar. Den tänkta miljön är motorvägskörning, främst därför att det innebär en hel del förenklingar i jämförelse med andra typer av vägar. Det stora problemet för sådana algoritmer ligger ofta i att sensorer lider av olika typer av nackdelar. De mäter bara en viss specifik sak, kan ha stora mätfel, är känsliga för olika förhållanden och har begränsingar i räckvidd. För att uttnyttja sensorernas olika styrkor och mildra effekten av deras brister har ett flertal sensorer använts tillsammans. Examensarbetet har utförts på Scania och testats på deras lastbilar. De typer av sensorer som har använts är redan, eller är på god väg att bli, standardutrustning i deras lastbilar och i många andra moderna fordon. Algoritmen använder sig av mätningar från en vägmarkeringsdetektor, som tillhandahåller formen på de två närmaste väglinjerna, en radar, som ger position och rörelse hos framförvarande bilar, en kartdatabas, som tillsammans med en GPS ger tidigare uppmätt kurvatur vid fordonets position, och interna sensorer som mäter det egna fordonets rörelser. För att kunna fortsätta ge en skattning när mätningar inte finns tillgängliga och för att göra algoritmen robustare mot dålig data, har en metod använts som uttnyttjar informationen i tidigare mätvärden, ett så kallat Extended Kalman filter. Denna metod kräver en matematisk beskrivning av hur formen på vägen framför fordonet förväntas förändras över tid, baserat på hur fordonet rör sig. De olika typerna av mätvärden från sensorerna kombineras i metoden och viktas olika beroende på hur tillförlitliga man anser att sensorerna är. Algoritmen har utvärderats på mätningar från allmänna motorvägar utanför Södertälje. Resultatet från denna utvärdering visar att det är väldigt fördelaktigt att kombinera flera olika typer av sensorer för att kunna leverera en bra skattning så ofta som möjligt. Det visar sig även att de olika typerna av sensorer är av olika stor betydelse för olika vägformsparametrar.

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Land vehicle navigation is primarily dependent upon the Global Positioning System (GPS) which provides accurate navigation in open sky. However, in urban and rural canyons GPS accuracy degrades considerably. To help GPS in such scenarios, it is often integrated with inexpensive inertial sensors. Such sensors have complex stochastic errors which are difficult to mitigate. In the presence of speed measurements from land vehicle, a reduced number of inertial sensors can be used which improve performance and termed as the Reduced Inertial Sensor System (RISS). Existing low-cost RISS/GPS integrated algorithms have limited accuracy due to use of approximations in error models and employment of a Linearized Kalman Filter (LKF). This research developed an enhanced error model for RISS which was integrated with GPS using an Extended Kalman Filter (EKF) for improved navigation of land vehicles. The proposed system was tested on several road experiments and the results confirmed the sustainable performance of the system during long GPS outages. To further increase the accuracy, Differential GPS (DGPS) is employed where carrier phase measurements are typically used. This requires resolution of Integer Ambiguity (IA) that comes at computational and time expense. This research uses pseudorange measurements for DGPS which mitigate large biases due to atmospheric errors and obviate the resolution of IA. These measurements are integrated with the enhanced RISS to filter increased noise and help GPS during signal blockages. The performance of the proposed system was compared with two other algorithms employing undifferenced GPS measurements where atmospheric effects are mitigated using either the Klobuchar model or dual frequency receivers. The proposed system performed better than both the algorithms in positional accuracy, multipath and GPS outages. This research further explored the reduction of Time-to-Fix Ambiguities (TTFA) for land vehicle navigation. To reduce the TTFA through inertial aiding, previous research used high-end Inertial Measurement Units (IMUs). This research uses MEMS grade IMU by integrating the enhanced RISS with carrier phase measurements using EKF. This algorithm was also tested on three road trajectories and it was shown that this integration helps reduce the TTFA as compared to the GPS-only case when fewer satellites are visible. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2014-06-23 11:30:58.036