Browsing by Subject "Navigation"
Now showing 1 - 10 of 10
Results Per Page
Sort Options
Item A Comparative Study of Kalman Filter Implementations for Relative GPS Navigation(2011-02-22) Fritz, Matthew PeytonRelative global positioning system (GPS) navigation is currently used for autonomous rendezvous and docking of two spacecraft as well as formation flying applications. GPS receivers deliver measurements to flight software that use this information to determine estimates of the current states of the spacecraft. The success of autonomous proximity operations in the presence of an uncertain environment and noisy measurements depends primarily on the navigation accuracy. This thesis presents the implementation and calibration of a spaceborne GPS receiver model, a visibility analysis for multiple GPS antenna cone angles, the implementation of four different extended Kalman filter architectures and a comparison of the advantages and disadvantages of each filter used for relative GPS navigation. A spaceborne GPS model is developed to generate simulated GPS measurements for a spacecraft located on any orbit around the Earth below the GPS constellation. Position and velocity estimation algorithms for GPS receivers are developed and implemented. A visibility analysis is performed to determine the number of visible satellites throughout the duration of the rendezvous. Multiple constant fields of view are analyzed and results compared to develop an understanding of how the GPS constellation evolves during the proximity operations. The comparison is used to choose a field of view with adequate satellite coverage. The advantages and disadvantages of the relative navigation architectures are evaluated based on a trade study involving several parameters. It is determined in this thesis that a reduced pseudorange filter provides the best overall performance in both relative and absolute navigation with less computational cost than the slightly more accurate pseudorange lter. A relative pseudorange architecture experiences complications due to multipath rich environments and performs well in only relative navigation. A position velocity architecture performs well in absolute state estimation but the worst of the four filters studied in relative state estimation.Item Analysis and demonstration: a proof-of-concept compass star tracker(Texas A&M University, 2007-04-25) Swanzy, Michael JohnThis research analyzes and demonstrates the local position determination problem on Earth using a novel instrument, the Compass Star Tracker. Special focus is given to the theoretical development of the mathematics of local position determination, the design and fabrication of a proof-of-concept instrument, an error source analysis, and the experimental tests used to validate the position determination concepts. Star sensors are typically used as attitude determination instruments on spacecraft orbiting Earth. In this capacity, the star sensor determines the orientation of the spacecraft using digital images of the stars. This research utilizes the basic functionality of the star sensor in a new way; the orientation information from the star image is used to determine a user's latitude and longitude coordinates on Earth. This concept is valuable because it allows users to determine their position autonomously. The fundamental concepts that enable local position determination were originally published in Drs. Samaan, Mortari, and Junkins (AAS 04-007). This research improves upon that work by eliminating the zenith-orientation constraint and providing several crucial theoretical corrections. In addition to the position determination mathematics, this research provides analysis of the theoretical and practical error sources associated with the position determination problem. This research also details the design, fabrication, and experimental test program of a proof-of-concept Compass Star Tracker. Together, the theoretical development, error analysis, instrument design, and test program serve as validation of the the position determination concept. This work is intended as the first of many steps toward eventual deployment of autonomous position determination sensors on the Moon and Mars.Item Analysis and order reduction of an autonomous lunar lander navigation system(2009-08) Newman, Clark Patrick; Bishop, Robert H., 1957-; Akella, Maruthi R.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.Item Analysis and synthesis of collaborative opportunistic navigation systems(2014-05) Kassas, Zaher; Humphreys, Todd Edwin; Arapostathis, Ari, 1954-Navigation is an invisible utility that is often taken for granted with considerable societal and economic impacts. Not only is navigation essential to our modern life, but the more it advances, the more possibilities are created. Navigation is at the heart of three emerging fields: autonomous vehicles, location-based services, and intelligent transportation systems. Global navigation satellite systems (GNSS) are insufficient for reliable anytime, anywhere navigation, particularly indoors, in deep urban canyons, and in environments under malicious attacks (e.g., jamming and spoofing). The conventional approach to overcome the limitations of GNSS-based navigation is to couple GNSS receivers with dead reckoning sensors. A new paradigm, termed opportunistic navigation (OpNav), is emerging. OpNav is analogous to how living creatures naturally navigate: by learning their environment. OpNav aims to exploit the plenitude of ambient radio frequency signals of opportunity (SOPs) in the environment. OpNav radio receivers, which may be handheld or vehicle-mounted, continuously search for opportune signals from which to draw position and timing information, employing on-the-fly signal characterization as necessary. In collaborative opportunistic navigation (COpNav), multiple receivers share information to construct and continuously refine a global signal landscape. For the sake of motivation, consider the following problem. A number of receivers with no a priori knowledge about their own states are dropped in an environment comprising multiple unknown terrestrial SOPs. The receivers draw pseudorange observations from the SOPs. The receivers' objective is to build a high-fidelity signal landscape map of the environment within which they localize themselves in space and time. We then ask: (i) Under what conditions is the environment fully observable? (ii) In cases where the environment is not fully observable, what are the observable states? (iii) How would receiver-controlled maneuvers affect observability? (iv) What is the degree of observability of the various states in the environment? (v) What motion planning strategy should the receivers employ for optimal information gathering? (vi) How effective are receding horizon strategies over greedy for receiver trajectory optimization, and what are their limitations? (vii) What level of collaboration between the receivers achieves a minimal price of anarchy? This dissertation addresses these fundamental questions and validates the theoretical conclusions numerically and experimentally.Item Design of a CubeSat guidance, navigation, and control module(2011-08) Kjellberg, Henri Christian; Lightsey, E. Glenn.; Fowler, Wallace T.A guidance, navigation, and control (GN&C) module is being designed and fabricated as part of a series of CubeSats being built by the Satellite Design Laboratory at the University of Texas. A spacecraft attitude control simulation environment called StarBox was created in order to perform trade studies and conduct performance analysis for the GN&C module. Navigation and control algorithms were tested using StarBox and then implemented onto an embedded flight computer. These algorithms were then tested in a hardware-in-the-loop simulation. In addition, the feasibility of utilizing advanced constrained attitude control algorithms was investigated by focusing on implementation in flight software. A mechanical and electrical design for the GN&C module was completed. A prototype system was set up on a bench-top for integrated testing. The analysis indicates that the system will satisfy the requirements of several CubeSat missions, including the current missions at the University of Texas known as Bevo2 and ARMADILLO.Item Information acquisition in navigation(2008-08) Nadeem, Sahar, 1981-; Stankiewicz, Brian J.; Hayhoe, MaryThe retention and recognition of landmarks within large-scale spaces (buildings or cities) plays an important role in way-finding and localization abilities. The current studies investigate our capacity for storing these views and the strategies used in deciding what information is stored and used. To investigate the issue of capacity we trained and tested subjects in six different environments with different levels of complexity. This manipulation was achieved by varying the number of states (position and orientations) within the environment from 10 to 132 in which each state generated a unique view. This manipulation generated environments in which the information content varied from 3 bits to 7.04 bits. We found no evidence of a capacity limitation for up to 7 bits of information. However, we did find that humans consistently lose about 1.25 bits of information regardless of the size of the environment. This finding was consistent in both virtual realty and in real environment. We further studied the nature of the information loss. Can gaze patterns reveal what information is being lost during the encoding process?Item Monte Carlo localization for robots using dynamically expanding occupancy grids(Texas Tech University, 2005-05) Gupta, Karan M.; Pyeatt, Larry D.; Watson, RichardThe past few years have seen tremendous growth in the research areas of Mobile Robotics. While growth has been fast and several problems have been very splendidly solved most mobile roboticists are faced with two primary challenges: how will the robot gather information about its environment and how will it know where it is? These two problems are referred to as:
(i). Mapping and
(ii). Localization.
Mapping is the process whereby a robot can extract relevant information from its environment allowing it to ”remember” it. Localization is using this stored map to move about in the environment with a clear sense of direction because the robot knows where it is, by referring to the map. Localization is the problem of estimating a robot’s pose relative to a map of its environment. However, both these problems are computationally intensive to solve and furthermore, limitations on a robot’s on board computational abilities and inaccuracies in sensor hardware and motor effectors make it even harder. Most mapping techniques are limited by memory and hence a robot has a limitation on the area that it can directly map. Also, if the mapped area is extended, most mapping implementations require that the mapping parameters be changed and the entire mapping algorithm be executed again. However, in recent times a new mapping technique was explored which is that of using Dynamically Expanding Occupancy Grids (Ellore 2002), and of using a Centralized Storage System (Barnes, Quasny, Garcia, and Pyeatt 2004). By using this approach, the robot has virtually unlimited storage space and a small initial map which grows as the robots explores its environment. Localization has not yet been attempted using Dynamically Expanding Occupancy Grids and a Centralised Storage System. This research is geared towards implementing Monte-Carlo Localization methods (Fox, Burgard, Dellaert, and Thrun 1999; Dellaert, Fox, Burgard, and Thrun ; Thrun, Fox, Burgard, and Dellaert 2001; Fox, Thrun, Burgard, and Dellaert 2001) to robots using Dynamically Expanding Occupancy Grids. By using this approach this research aims to provide a complete mapping and localization implementation for robots using dynamically expanding occupancy grids and a centralized storage system.Item Monte Carlo localization for robots using dynamically expanding occupancy grids(2005-05) Gupta, Karan M.; Pyeatt, Larry D.; Watson, RichardThe past few years have seen tremendous growth in the research areas of Mobile Robotics. While growth has been fast and several problems have been very splendidly solved most mobile roboticists are faced with two primary challenges: how will the robot gather information about its environment and how will it know where it is? These two problems are referred to as: (i). Mapping and (ii). Localization. Mapping is the process whereby a robot can extract relevant information from its environment allowing it to "remember" it. Localization is using this stored map to move about in the environment with a clear sense of direction because the robot knows where it is, by referring to the map. Localization is the problem of estimating a robot’s pose relative to a map of its environment. However, both these problems are computationally intensive to solve and furthermore, limitations on a robot’s on board computational abilities and inaccuracies in sensor hardware and motor effectors make it even harder. Most mapping techniques are limited by memory and hence a robot has a limitation on the area that it can directly map. Also, if the mapped area is extended, most mapping implementations require that the mapping parameters be changed and the entire mapping algorithm be executed again. However, in recent times a new mapping technique was explored which is that of using Dynamically Expanding Occupancy Grids (Ellore 2002), and of using a Centralized Storage System (Barnes, Quasny, Garcia, and Pyeatt 2004). By using this approach, the robot has virtually unlimited storage space and a small initial map which grows as the robots explores its environment. Localization has not yet been attempted using Dynamically Expanding Occupancy Grids and a Centralised Storage System. This research is geared towards implementing Monte-Carlo Localization methods (Fox, Burgard, Dellaert, and Thrun 1999; Dellaert, Fox,Burgard, and Thrun ; Thrun, Fox, Burgard, and Dellaert 2001; Fox, Thrun, Burgard, and Dellaert 2001) to robots using Dynamically Expanding Occupancy Grids. By using this approach this research aims to provide a complete mapping and localization implementation for robots using dynamically expanding occupancy grids and a centralized storage system.Item Navigation filter design and comparison for Texas 2 STEP nanosatellite(2009-12) Wright, Cinnamon Amber; Lightsey, E. Glenn; Bishop, Robert H.A Discrete Extended Kalman Filter has been designed to process measurements from a magnetometer, sun sensor, IMU, and GPS receiver to provide the relative position, velocity, attitude, and gyro bias of a chaser spacecraft relative to a target spacecraft. An Extended Kalman Filter with Uncompensated Bias has also been developed for the implementation of well known biases and errors that are not directly observable. A detailed explanation of the algorithms, models, and derivations that go into both filters is presented. With this simulation and specific sensor selection the position of the chaser spacecraft relative to the target can be estimated to within about 5 m, the velocity to within .1 m/s, and the attitude to within 2 degrees for both filters. If a thrust is applied to the IMU measurements, it takes about 1.5 minutes to get a good position estimate, using the Extended Kalman Filter with Uncompensated Bias. The error settles almost immediately using the general Extended Kalman Filter. These filters have been designed for and can be implemented on almost any small, low cost, low power satellite with this inexpensive set of sensors.Item Navigation solution for the Texas A&M autonomous ground vehicle(Texas A&M University, 2006-10-30) Odom, Craig AllenThe need addressed in this thesis is to provide an Autonomous Ground Vehicle (AGV) with accurate information regarding its position, velocity, and orientation. The system chosen to meet these needs incorporates (1) a differential Global Positioning System, (2) an Inertial Measurement Unit consisting of accelerometers and angular-rate sensors, and (3) a Kalman Filter (KF) to fuse the sensor data. The obstacle avoidance software requires position and orientation to build a global map of obstacles based on the returns of a scanning laser rangefinder. The path control software requires position and velocity. The development of the KF is the major contribution of this thesis. This technology can either be purchased or developed, and, for educational and financial reasons, it was decided to develop instead of purchasing the KF software. This thesis analyzes three different cases of navigation: one-dimensional, two dimensional and three-dimensional (general). Each becomes more complex, and separating them allows a three step progression to reach the general motion solution. Three tests were conducted at the Texas A&M University Riverside campus that demonstrated the accuracy of the solution. Starting from a designated origin, the AGV traveled along the runway and then returned to the same origin within 11 cm along the North axis, 19 cm along the East axis and 8 cm along the Down axis. Also, the vehicle traveled along runway 35R which runs North-South within 0.1????, with the yaw solution consistently within 1???? of North or South. The final test was mapping a box onto the origin of the global map, which requires accurate linear and angular position estimates and a correct mapping transformation.