Thesis (M. Eng.(Mechanical))-University of Pretoria, 2005. / Includes bibliographical references. Available on the Internet via the World Wide Web.

This thesis deals with the challenge of autonomous navigation of the ExoMars rover. The absence of global positioning systems (GPS) in space, added to the limitations of wheel odometry makes autonomous navigation based on these two techniques - as done in the literature - an inviable solution and necessitates the use of other approaches. That, among other reasons, motivates this work to use solely visual data to solve the robot’s Egomotion problem. The homogeneity of Mars’ terrain makes the robustness of the low level image processing technique a critical requirement. In the first part of the thesis, novel solutions are presented to tackle this specific problem. Detection of robust features against illumination changes and unique matching and association of features is a sought after capability. A solution for robustness of features against illumination variation is proposed combining Harris corner detection together with moment image representation. Whereas the first provides a technique for efficient feature detection, the moment images add the necessary brightness invariance. Moreover, a bucketing strategy is used to guarantee that features are homogeneously distributed within the images. Then, the addition of local feature descriptors guarantees the unique identification of image cues. In the second part, reliable and precise motion estimation for the Mars’s robot is studied. A number of successful approaches are thoroughly analysed. Visual Simultaneous Localisation And Mapping (VSLAM) is investigated, proposing enhancements and integrating it with the robust feature methodology. Then, linear and nonlinear optimisation techniques are explored. Alternative photogrammetry reprojection concepts are tested. Lastly, data fusion techniques are proposed to deal with the integration of multiple stereo view data. Our robust visual scheme allows good feature repeatability. Because of this, dimensionality reduction of the feature data can be used without compromising the overall performance of the proposed solutions for motion estimation. Also, the developed Egomotion techniques have been extensively validated using both simulated and real data collected at ESA-ESTEC facilities. Multiple stereo view solutions for robot motion estimation are introduced, presenting interesting benefits. The obtained results prove the innovative methods presented here to be accurate and reliable approaches capable to solve the Egomotion problem in a Mars environment. / © Cranfield University

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Flooding can severely disrupt transportation systems. When safety measures are limited to road closures, vehicles affected by the flooding have an origin, destination, or path segment that is closed or soon-to-be flooded during the trip's duration. This thesis introduces a framework to provide routing assistance and trip cancellation recommendations to affected vehicles. The framework relies on the connected vehicle environment for real-time link performance measures and flood data and evaluates the trip of the vehicle to determine whether it is affected by the flood or not. If the vehicle is affected and can still leave its origin, the framework generates the corresponding routing assistance in the form of hyperpath(s) or set of alternative paths. On the other hand, a vehicle with a closed origin receives a warning to wait at origin, while a vehicle with an affected destination is assigned to a new safe one. This framework is tested on two transportation networks. The evaluation of the framework's scalability to different network sizes and the sensitivity of the results to various flood characteristics, policy-related variables and other dependencies are performed using simulated vehicle data and hypothetical flood scenarios. The computation times depends on the network size and flood depth but have generally an average of 1.47 seconds for the largest tested network and deepest tested flood. The framework has the potential to alleviate the impacts and inconveniences associated with flooding. / Master of Science / Flooding is a natural hazard that occurs with heavy rainfalls and high tides. In extreme situations, a flood in an area results in the evacuation of its occupant. Yet, in many cases, a flood is less severe and may only result in roads closures without necessitating evacuation. During these situations, and as transportation engineers, our ultimate goal is to maintain efficient and safe traffic operations. This thesis introduces a framework that focuses on providing routing assistance to affected vehicles and sending warnings to unaffected ones. It relies on the future connected vehicle environment which enables the communication between a traffic management center and equipped vehicles. The traffic management center collects and processes the information about the link performance measures and the weather and flood forecasts and sends them to the connected vehicles. Each vehicle has an in-vehicle navigation system in which the proposed framework is embedded. The framework, depending on the vehicle’s origin, destination, path and departure time and based on the flood’s characteristics, determines whether the vehicle is affected or not. If the vehicle is unaffected, it will receive a warning with the areas to avoid in case of any deviation and it can resume its trip as intended. If affected, the vehicle will either receive a warning to stay at its origin or routing guidance in the form of hyperpath or a set of alternative paths. The proposed framework has been evaluated on two transportation networks modeled in VISSIM based on the city of Virginia Beach, VA. Using simulated vehicle data and generated flood scenarios, several tests were executed to evaluate the scalability of the framework to different transportation networks along with the sensitivity of the results to variation in flood characteristics, policy-dependent variables and other dependencies. Concentrated, more intense and deeper floods resulted in a higher impact on the system. Yet, the analysis of the output is highly dependent on the location of the origin and destination of the vehicles with respect to the flooded roads. Thus, a lot of the output explanation are specific. Computation time increased with the increase in network size and in the flood depth. Nevertheless, it is still small and reasonable and further increase in both parameters (network size and flood depth) can be tested in future along with multiple techniques that minimize the computation time. This framework addresses the flooding hazard which road users are experiencing more and more nowadays. This hazard brings risks and inconveniences to our daily life. Thus, the development of this framework is of great interest to our society as it is a promising tool that has the potential to offer benefits, in terms of safety and mobility, to roads users exposed to a flood hazard. Its first implementation shows that it is a timely application with a potential to perform even better with future improvements.

Inertial navigation systems require a precise knowledge of gravity to function properly. The inability of models to account for the small amplitude, short wavelength components of the gravity field leads to errors which are frequently viewed as random; these random errors can introduce a significant cumulative impact on system performance. A model is studied which, in the context of an appropriate scaling, consists of a gravity field having a known deteministic long scale behavior and an unknown random short scale behavior. The short wavelength random fluctuations are assumed to satisfy a strong mixing (asymptotic independence) property; no a priori stationary or isotropy assumptions are made. Results of Khas'minskii (Theory of Probability and Its Applications, Vol. XI, No. 2, 1966, pp 211-228) are extended and applied. In an appropriate asymptotic limit, the vehicle motion is approximated by the sum of a deterministic trajectory and a Gauss-Markov fluctuation process. / Ph. D.

International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California / A recurring problem in flight testing navigation systems is the need for an accurate, common time reference for the system under test and for the truth source to which it is compared. Lockheed Martin Aeronautics Company and Computer Sciences Corporation have developed software that utilizes all available timing information to reference the times of validity for each navigation measurement to Coordinated Universal Time. This permits accurate comparison and correlation of data necessary for statistical error analysis of the navigation system.

This work investigates the possibility of obtaining attitude estimates by capturing images of stars using a SWIR camera. Today, many autonomous systems rely on the measurements from a GPS to obtain accurate position and attitude estimates. However, the GPS signals are vulnerable to both jamming and spoofing, making any system reliant on only GPS signals insecure. To make the navigation systems more robust, other sensors can be added to acquire a multisensor system. One of these sensors might be a ground based SWIR star camera that is able to provide accurate attitude estimates. To investigate if this is possible, an experimental setup with a SWIR camera was placed at the office of FOI Linköping, where the camera in a rigid position has captured images of the sky. The SWIR camera possesses several advantages over a camera operating in the visual spectrum. For example, the background radiation is weaker and the transmission through the atmosphere is higher in certain wavelength bands.  The images captured by the SWIR camera was provided to a star tracker software that has been developed. The star tracker software contains algorithms to detect stars, position them in the image at subpixel accuracy, match the stars to a star database and finally output an attitude based on the stars from the image and the identified stars in the database. To further improve the attitude estimates, an MEKF was applied. The results show that attitude estimates could be obtained consistently from late evenings to early mornings, when the sky was dark. However, this required that the weather conditions were good, i.e., a limited amount of clouds. When more clouds were present, no attitude estimates could be provided for a majority of the night. The SWIR camera was also compared to a camera operating in the visual spectrum when clouds were present, to see if the results were any different. With the camera settings applied in this work, the two cameras seemed to perform equally. The accuracy of the estimated attitudes is hard to validate, since no true attitude is available. However, the variance of the estimates was low, and the major differences in the attitude estimates over a night's measurements seemed to be a drift present in all angles. The maximum estimated error in declination during a night's measurements varied from about 40 to 60 arc seconds, depending on the data set. The maximum estimated error in right ascension varied between 200 and 2000 arc seconds, and the same metric in the roll estimate were about 100 to 2500 arc seconds. The reason for the drifts is assumed to be atmospheric effects not being accounted for, and astronomical effects moving the direction of the rotation axis of the earth, creating errors in the star positions given in the database.

Standard GPS receivers are unable to provide the rate or precision required when used on a small vessel such as an Unmanned Surface Vehicles (USVs). To overcome this, the thesis presents a low cost high rate motion measurement system for an USV with underwater and oceanographic purposes. The work integrates an Inertial Measurement Unit (IMU), a GPS receiver, a flux-gate compass, a tilt sensor and develops a software package, using real time data fusion methods, for an USV to aid in the navigation and control as well as controlling an onboard Acoustic Doppler Current Profiler (ADCP).While ADCPs non-intrusively measure water flow, they suffer from the inability to discriminate between motions in the water column and self-motion. Thus, the vessel motion contamination needs to be removed to analyze the data and the system developed in this thesis provides the motion measurements and processing to accomplish this task. / by Chrystel Gelin. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

The design, implementation, and testing of an experimental setup intended to evaluate the dynamic maneuvering performance of the Wave Adaptive Modular Vessel (WAM-V) class USV12, a 3.7 meter unmanned surface vehicle (USV) is described. A comprehensive sensor package was designed, fabricated and assembled to record the vehicle's dynamic response to various control inputs. All subsystems were fabricated and installed on a test vehicle, GUSS, and full system, open-loop maneuvering tests were conducted to show validity of data collection technique. Simulations were performed using model parameters found in the literature to create a "simulated experimental" data set, upon which system identification techniques were used to rediscover a suitable model with similar parameterization. Combined, the sensor package and the method for creating this model support future work in the design of automatic control, navigation, and guidance systems for the WAM-V USV12. / by Janine L. Mask. / Thesis (M.S.C.S.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.