Kalman Filtering and Its Real‐Time Applications

Kalman filter was pioneered by Rudolf Emil Kalman in 1960, originally designed and developed to solve the navigation problem in Apollo Project. Since then, numerous applications were developed with the implementation of Kalman filter, such as applications in the fields of navigation and computer vision\u27s object tracking. Kalman filter consists of two separate processes, namely the prediction process and the measurement process, which work in a recursive manner. Both processes are modeled by groups of equations in the state space model to achieve optimal estimation outputs. Prior knowledge on the state space model is needed, and it differs between different systems. In this chapter, the authors outlined and explained the fundamental Kalman filtering model in real‐time discrete form and devised two real‐time applications that implemented Kalman filter. The first application involved using vision camera to perform real‐time image processing for vehicle tracking, whereas the second application discussed the real‐time Global Positioning System (GPS)‐aided Strapdown Inertial Navigation Unit (SINU) system implementation using Kalman filter. Detail descriptions, model derivations, and results are outlined in both applications

Modelling and control of an articulated underground mining vehicle

The automation of the tramming or load, haul and dump (LHD) procedure, performed by a LHD vehicle, holds the potential to improve productivity, efficiency and safety in the mining environment. Productivity is mainly increased by longer working hours; efficiency is improved by repetitive, faultless and predictable work; and safety is improved by removing the human operator from the harsh environment. However, before the automation of the process can be addressed, a thorough understanding of the process and its duty in the overall mining method is required. Therefore, the current applicable mining methods and their areas of potential automation are given. Since the automation of the LHD vehicle is at the core of this project, its implementation in the tramming process is also justified. Also, the current underground navigation methods are given and their shortcomings are named. It is concluded that infrastructure-free navigation is the only viable solution in the ever-changing mining environment. With that in mind, the feasibility of various navigation sensors is discussed and conclusions are drawn. Both kinematic and dynamic modelling of LHD vehicles are introduced. Various forms of kinematic models are given and their underlying modelling assumptions are named. The most prominent assumptions concern the vehicle’s half-length and the inclusion of a wheel-slip factor. Dynamic modelling techniques, with a strong emphasis on tyre modelling, are also stated. In order to evaluate the modelling techniques, field tests are performed on the articulated vehicles, namely the Wright 365 LHD and the Bell 1706C loader. The test on the Wright 365 LHD gives a good impression of the harsh ergonomics under which the operator has to work. A more thorough test is performed on the Bell 1706C articulated loader. The test results are then compared to simulation results obtained from the kinematic models. Also, the above-named assumptions are tested, evaluated and discussed. A dynamic model is also simulated and discussed. Lastly, two localization and control methods are given and evaluated. The first method is an open-loop nonlinear optimal control strategy with periodic position resetting and the second method is a pathtracking controller. AFRIKAANS : Automatisering van die laai-, vervoer- en dompel- (LVD) prosedure het die potensiaal om die produktiwiteit, effektiwiteit en veiligheid van die mynbedryf te verbeter. Produktiwiteit word hoofsaaklik deur langer werksure verhoog, effektiwiteit word deur herhalende, foutlose en voorspelbare werk verbeter en veiligheid word verbeter omdat menslike operateurs uit die gevaarlike ondergrondse omgewing verwyder word. Voordat aandag aan die automatisering van die prosedure geskenk kan word, moet die prosedure en die algemene mynbedrywighede rakende die prosedure deeglik bestudeer en verstaan word. As gevolg hiervan word die huidige, toepaslike mynboumetodes hier gedokumenteer. Die implementering van ʼn gekoppelde LVD-voertuig in die LVD-prosesword ook geregverdig. Verder word die huidige metodes van ondergrondse navigasie genoem en hulle tekortkominge aangedui. Die gevolgtrekking dat infrastruktuur-vrye navigasie die enigste lewensvatbare navigasiemetode in die immer veranderende ondergrondsemynbouomgewing is, word ook gemaak. In die lig daarvan word ʼn verskeidenheid sensors genoem en bespreek. Kinematiese en dinamiese modellering van ʼn LVD-voertuig word bekendgestel. Verskeie kinematiese modelle en hulle onderliggende aannames word genoem. Die mees prominente aannames is die lengte van die gekoppelde voertuig se hoofdele en die insluiting van ʼn wielglipfaktor. Die tegnieke van dinamiese modellering, met die klem op bandmodellering, word ook gegee. Praktyktoetse op gekoppelde voertuie is ook gedoen om die verskillende modelle te evalueer. Die toets op die Wright 365-LVD bied goeie insig in die strawwe ergonomiese toestande waaronder die operateurs moet werk. ʼn Deeglike toets is op ʼn BELL 1706C- gekoppelde laaier, wat kinematies identies aan ʼn LVD-voertuig is, uitgevoer. Die bevindinge van die toets word met bogenoemde modelsimulasies vergelyk en gevolgtrekkings word gemaak. Laastens word lokalisiering en beheer van ʼn LVDvoertuig behandel. Twee beheermetodes, opelus- nie-lineêre optimale beheer met periodieke herstel en padvolgingbeheer word geëvalueer en bespreek. CopyrightDissertation (MEng)--University of Pretoria, 2012.Electrical, Electronic and Computer Engineeringunrestricte

Communication-based UAV Swarm Missions

Unmanned aerial vehicles have developed rapidly in recent years due to technological advances. UAV technology can be applied to a wide range of applications in surveillance, rescue, agriculture and transport. The problems that can exist in these areas can be mitigated by combining clusters of drones with several technologies. For example, when a swarm of drones is under attack, it may not be able to obtain the position feedback provided by the Global Positioning System (GPS). This poses a new challenge for the UAV swarm to fulfill a specific mission. This thesis intends to use as few sensors as possible on the UAVs and to design the smallest possible information transfer between the UAVs to maintain the shape of the UAV formation in flight and to follow a predetermined trajectory. This thesis presents Extended Kalman Filter methods to navigate autonomously in a GPS-denied environment. The UAV formation control and distributed communication methods are also discussed and given in detail

A feasibility study: Forest Fire Advanced System Technology (FFAST)

The National Aeronautics and Space Administration/Jet Propulsion Laboratory and the United States Department of Agriculture Forest Service completed a feasibility study that examined the potential uses of advanced technology in forest fires mapping and detection. The current and future (1990's) information needs in forest fire management were determined through interviews. Analysis shows that integrated information gathering and processing is needed. The emerging technologies that were surveyed and identified as possible candidates for use in an end to end system include ""push broom'' sensor arrays, automatic georeferencing, satellite communication links, near real or real time image processing, and data integration. Matching the user requirements and the technologies yielded a ""strawman'' system configuration. The feasibility study recommends and outlines the implementation of the next phase for this project, a two year, conceptual design phase to define a system that warrants continued development

Sensor fusion and noise modeling for improved vehicle localization

This thesis examines the application of sensor fusion technique for the real-time vehicle localization improvement. The Kalman filtering technique has been used for the fusion process in this research. Previous researchers found Kalman filtering creditable in removing the white noises in sensor measurements but very few of them explained the fine tuning of the system and measurement noise matrices which are the foremost performance decisive parameters in the recursive Kalman filter algorithm. In the first application, the Kalman filtering was used to improve the navigational context recognization ability of an autonomous robot when the robot is navigating in simulated tree plantation nursery. An Extended Kalman Filtering (EKF) algorithm was developed and implemented to improve the accuracy of posture estimation of a skid-steered autonomous robot. A kinematic system model consisting of seven states was developed for implementing an EKF algorithm. The GPS error along with external vibration noise and Dynamic Measurement Unit\u27s static bias drift was found to be the main sources of error affecting the position and heading of the robot vehicle. In addition to the EKF, a second order autoregression error model was developed to model the real-time kinematic-GPS (RTK-GPS) errors. The EKF with Autoregressive error model enhanced robot\u27s localization accuracy over the EKF without incorporating an error model. Furthermore, the developed filtering and K-means clustering algorithms were successful in recognizing and reconstructing the navigational context of an autonomous weeding robot in a simulated tree plantation nursery. The second application of EKF was for improving the attitude angle estimates of the self-propelled sprayer by fusing the roll and pitch estimates from a digital elevation model (DEM) with the roll measurements from terrain compensation module sensor (TCM) and pitch estimates from a single GPS sensor. The EKF algorithm was capable of estimating the sprayer attitude angles even when the DEMs attitude estimates were not available for a certain period due to the out of bound circumstance of the DEM. The EKF and AR error model algorithms were also capable of removing the high frequency noise associated with the TCM and GPS sensor measurements

Guidance, flight mechanics and trajectory optimization. Volume 14 - Entry guidance equations

Entry guidance equations, reference trajectories, and guidance system mechanizatio

Avionics architecture studies for the entry research vehicle

This report is the culmination of a year-long investigation of the avionics architecture for NASA's Entry Research Vehicle (ERV). The Entry Research Vehicle is conceived to be an unmanned, autonomous spacecraft to be deployed from the Shuttle. It will perform various aerodynamic and propulsive maneuvers in orbit and land at Edwards AFB after a 5 to 10 hour mission. The design and analysis of the vehicle's avionics architecture are detailed here. The architecture consists of a central triply redundant ultra-reliable fault tolerant processor attached to three replicated and distributed MIL-STD-1553 buses for input and output. The reliability analysis is detailed here. The architecture was found to be sufficiently reliable for the ERV mission plan

SIRU utilization. Volume 1: Theory, development and test evaluation

The theory, development, and test evaluations of the Strapdown Inertial Reference Unit (SIRU) are discussed. The statistical failure detection and isolation, single position calibration, and self alignment techniques are emphasized. Circuit diagrams of the system components are provided. Mathematical models are developed to show the performance characteristics of the subsystems. Specific areas of the utilization program are identified as: (1) error source propagation characteristics and (2) local level navigation performance demonstrations

System configuration and executive requirements specifications for reusable shuttle and space station/base

System configuration and executive requirements specifications for reusable shuttle and space station/bas