999 resultados para Navigation solution
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Trabalho final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações
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This work analyses a real time orbit estimator using the raw navigation solution provided by GPS receivers. The estimation algorithm considers a Kalman filter with a rather simple orbit dynamic model and random walk modeling of the receiver clock bias and drift. Using the Topex/Poseidon satellite as test bed, characteristics of model truncation, sampling rates and degradation of the GPS receiver (Selective Availability) were analysed. Copyright © 2007 by ABCM.
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In this chapter a low-cost surgical navigation solution for periacetabular osteotomy (PAO) surgery is described. Two commercial inertial measurement units (IMU, Xsens Technologies, The Netherlands), are attached to a patient’s pelvis and to the acetabular fragment, respectively. Registration of the patient with a pre-operatively acquired computer model is done by recording the orientation of the patient’s anterior pelvic plane (APP) using one IMU. A custom-designed device is used to record the orientation of the APP in the reference coordinate system of the IMU. After registration, the two sensors are mounted to the patient’s pelvis and acetabular fragment, respectively. Once the initial position is recorded, the orientation is measured and displayed on a computer screen. A patient-specific computer model generated from a pre-operatively acquired computed tomography (CT) scan is used to visualize the updated orientation of the acetabular fragment. Experiments with plastic bones (7 hip joints) performed in an operating room comparing a previously developed optical navigation system with our inertial-based navigation system showed no statistical difference on the measurement of acetabular component reorientation (anteversion and inclination). In six out of seven hip joints the mean absolute difference was below five degrees for both anteversion and inclination.
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PURPOSE To evaluate a low-cost, inertial sensor-based surgical navigation solution for periacetabular osteotomy (PAO) surgery without the line-of-sight impediment. METHODS Two commercial inertial measurement units (IMU, Xsens Technologies, The Netherlands), are attached to a patient's pelvis and to the acetabular fragment, respectively. Registration of the patient with a pre-operatively acquired computer model is done by recording the orientation of the patient's anterior pelvic plane (APP) using one IMU. A custom-designed device is used to record the orientation of the APP in the reference coordinate system of the IMU. After registration, the two sensors are mounted to the patient's pelvis and acetabular fragment, respectively. Once the initial position is recorded, the orientation is measured and displayed on a computer screen. A patient-specific computer model generated from a pre-operatively acquired computed tomography scan is used to visualize the updated orientation of the acetabular fragment. RESULTS Experiments with plastic bones (eight hip joints) performed in an operating room comparing a previously developed optical navigation system with our inertial-based navigation system showed no statistically significant difference on the measurement of acetabular component reorientation. In all eight hip joints the mean absolute difference was below four degrees. CONCLUSION Using two commercially available inertial measurement units we show that it is possible to accurately measure the orientation (inclination and anteversion) of the acetabular fragment during PAO surgery and therefore to successfully eliminate the line-of-sight impediment that optical navigation systems have.
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A range constraint method viz. centroid method is proposed to fuse the navigation information of dual (right and left) foot-mounted Zero-velocity-UPdaTe (ZUPT)-aided Inertial Navigation Systems (INSs). Here, the range constraint means that the distance of separation between the position estimates of right and left foot ZUPT-aided INSs cannot be greater than a quantity known as foot-to-foot maximum separation. We present the experimental results which illustrate the applicability of the proposed method. The results show that the proposed method significantly enhances the accuracy of the navigation solution when compared to using two uncoupled foot-mounted ZUPT-aided INSs. Also, we compare the performance of the proposed method with the existing data fusion methods.
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An Approach with Vertical Guidance (APV) is an instrument approach procedure which provides horizontal and vertical guidance to a pilot on approach to landing in reduced visibility conditions. APV approaches can greatly reduce the safety risk to general aviation by improving the pilot’s situational awareness. In particular the incidence of Controlled Flight Into Terrain (CFIT) which has occurred in a number of fatal air crashes in general aviation over the past decade in Australia, can be reduced. APV approaches can also improve general aviation operations. If implemented at Australian airports, APV approach procedures are expected to bring a cost saving of millions of dollars to the economy due to fewer missed approaches, diversions and an increased safety benefit. The provision of accurate horizontal and vertical guidance is achievable using the Global Positioning System (GPS). Because aviation is a safety of life application, an aviation-certified GPS receiver must have integrity monitoring or augmentation to ensure that its navigation solution can be trusted. However, the difficulty with the current GPS satellite constellation alone meeting APV integrity requirements, the susceptibility of GPS to jamming or interference and the potential shortcomings of proposed augmentation solutions for Australia such as the Ground-based Regional Augmentation System (GRAS) justifies the investigation of Aircraft Based Augmentation Systems (ABAS) as an alternative integrity solution for general aviation. ABAS augments GPS with other sensors at the aircraft to help it meet the integrity requirements. Typical ABAS designs assume high quality inertial sensors to provide an accurate reference trajectory for Kalman filters. Unfortunately high-quality inertial sensors are too expensive for general aviation. In contrast to these approaches the purpose of this research is to investigate fusing GPS with lower-cost Micro-Electro-Mechanical System (MEMS) Inertial Measurement Units (IMU) and a mathematical model of aircraft dynamics, referred to as an Aircraft Dynamic Model (ADM) in this thesis. Using a model of aircraft dynamics in navigation systems has been studied before in the available literature and shown to be useful particularly for aiding inertial coasting or attitude determination. In contrast to these applications, this thesis investigates its use in ABAS. This thesis presents an ABAS architecture concept which makes use of a MEMS IMU and ADM, named the General Aviation GPS Integrity System (GAGIS) for convenience. GAGIS includes a GPS, MEMS IMU, ADM, a bank of Extended Kalman Filters (EKF) and uses the Normalized Solution Separation (NSS) method for fault detection. The GPS, IMU and ADM information is fused together in a tightly-coupled configuration, with frequent GPS updates applied to correct the IMU and ADM. The use of both IMU and ADM allows for a number of different possible configurations. Three are investigated in this thesis; a GPS-IMU EKF, a GPS-ADM EKF and a GPS-IMU-ADM EKF. The integrity monitoring performance of the GPS-IMU EKF, GPS-ADM EKF and GPS-IMU-ADM EKF architectures are compared against each other and against a stand-alone GPS architecture in a series of computer simulation tests of an APV approach. Typical GPS, IMU, ADM and environmental errors are simulated. The simulation results show the GPS integrity monitoring performance achievable by augmenting GPS with an ADM and low-cost IMU for a general aviation aircraft on an APV approach. A contribution to research is made in determining whether a low-cost IMU or ADM can provide improved integrity monitoring performance over stand-alone GPS. It is found that a reduction of approximately 50% in protection levels is possible using the GPS-IMU EKF or GPS-ADM EKF as well as faster detection of a slowly growing ramp fault on a GPS pseudorange measurement. A second contribution is made in determining how augmenting GPS with an ADM compares to using a low-cost IMU. By comparing the results for the GPS-ADM EKF against the GPS-IMU EKF it is found that protection levels for the GPS-ADM EKF were only approximately 2% higher. This indicates that the GPS-ADM EKF may potentially replace the GPS-IMU EKF for integrity monitoring should the IMU ever fail. In this way the ADM may contribute to the navigation system robustness and redundancy. To investigate this further, a third contribution is made in determining whether or not the ADM can function as an IMU replacement to improve navigation system redundancy by investigating the case of three IMU accelerometers failing. It is found that the failed IMU measurements may be supplemented by the ADM and adequate integrity monitoring performance achieved. Besides treating the IMU and ADM separately as in the GPS-IMU EKF and GPS-ADM EKF, a fourth contribution is made in investigating the possibility of fusing the IMU and ADM information together to achieve greater performance than either alone. This is investigated using the GPS-IMU-ADM EKF. It is found that the GPS-IMU-ADM EKF can achieve protection levels approximately 3% lower in the horizontal and 6% lower in the vertical than a GPS-IMU EKF. However this small improvement may not justify the complexity of fusing the IMU with an ADM in practical systems. Affordable ABAS in general aviation may enhance existing GPS-only fault detection solutions or help overcome any outages in augmentation systems such as the Ground-based Regional Augmentation System (GRAS). Countries such as Australia which currently do not have an augmentation solution for general aviation could especially benefit from the economic savings and safety benefits of satellite navigation-based APV approaches.
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For many years, computer vision has lured researchers with promises of a low-cost, passive, lightweight and information-rich sensor suitable for navigation purposes. The prime difficulty in vision-based navigation is that the navigation solution will continually drift with time unless external information is available, whether it be cues from the appearance of the scene, a map of features (whether built online or known a priori), or from an externally-referenced sensor. It is not merely position that is of interest in the navigation problem. Attitude (i.e. the angular orientation of a body with respect to a reference frame) is integral to a visionbased navigation solution and is often of interest in its own right (e.g. flight control). This thesis examines vision-based attitude estimation in an aerospace environment, and two methods are proposed for constraining drift in the attitude solution; one through a novel integration of optical flow and the detection of the sky horizon, and the other through a loosely-coupled integration of Visual Odometry and GPS position measurements. In the first method, roll angle, pitch angle and the three aircraft body rates are recovered though a novel method of tracking the horizon over time and integrating the horizonderived attitude information with optical flow. An image processing front-end is used to select several candidate lines in a image that may or may not correspond to the true horizon, and the optical flow is calculated for each candidate line. Using an Extended Kalman Filter (EKF), the previously estimated aircraft state is propagated using a motion model and a candidate horizon line is associated using a statistical test based on the optical flow measurements and location of the horizon in the image. Once associated, the selected horizon line, along with the associated optical flow, is used as a measurement to the EKF. To evaluate the accuracy of the algorithm, two flights were conducted, one using a highly dynamic Uninhabited Airborne Vehicle (UAV) in clear flight conditions and the other in a human-piloted Cessna 172 in conditions where the horizon was partially obscured by terrain, haze and smoke. The UAV flight resulted in pitch and roll error standard deviations of 0.42° and 0.71° respectively when compared with a truth attitude source. The Cessna 172 flight resulted in pitch and roll error standard deviations of 1.79° and 1.75° respectively. In the second method for estimating attitude, a novel integrated GPS/Visual Odometry (GPS/VO) navigation filter is proposed, using a structure similar to a classic looselycoupled GPS/INS error-state navigation filter. Under such an arrangement, the error dynamics of the system are derived and a Kalman Filter is developed for estimating the errors in position and attitude. Through similar analysis to the GPS/INS problem, it is shown that the proposed filter is capable of recovering the complete attitude (i.e. pitch, roll and yaw) of the platform when subjected to acceleration not parallel to velocity for both the monocular and stereo variants of the filter. Furthermore, it is shown that under general straight line motion (e.g. constant velocity), only the component of attitude in the direction of motion is unobservable. Numerical simulations are performed to demonstrate the observability properties of the GPS/VO filter in both the monocular and stereo camera configurations. Furthermore, the proposed filter is tested on imagery collected using a Cessna 172 to demonstrate the observability properties on real-world data. The proposed GPS/VO filter does not require additional restrictions or assumptions such as platform-specific dynamics, map-matching, feature-tracking, visual loop-closing, gravity vector or additional sensors such as an IMU or magnetic compass. Since no platformspecific dynamics are required, the proposed filter is not limited to the aerospace domain and has the potential to be deployed in other platforms such as ground robots or mobile phones.
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El continuo crecimiento de la demanda del transporte aéreo, junto con los nuevos escenarios de intervención militar, están obligando a una optimización en el uso del espacio aéreo. De este modo, la UE y los EEUU (a través de SESAR y NextGen respectivamente) han asentado las bases para una nueva gestión del tráfico aéreo (ATM). Con ello, se pretende aumentar la capacidad de aeropuertos y rutas aéreas, otorgando mayor flexibilidad al uso del espacio aéreo sin comprometer la seguridad de los usuarios. Desde un punto de vista puramente técnico, la clave de este cambio de modelo está en el conocimiento de la posición de cada aeronave en cada instante. En este sentido, la tendencia en ATM es el uso de ADS-B como fuente principal de posicionamiento. Sin embargo, debido a que este sistema está basado en la difusión de la posición obtenida a través de GPS, es necesario un sistema de seguimiento independiente. Actualmente, la intención es migrar del radar secundario de vigilancia (SSR) a la multilateración de área extensa (WAM), con el fin de mejorar la integridad de la posición para aplicaciones en ruta. Aprovechando el rápido despliegue de ADS-B, se pretende reutilizar sus estaciones base para WAM. Cada estación base que recibe el mensaje ADS-B de la aeronave envía conjuntamente la medida del tiempo de llegada (TOA) de dicho mensaje al centro de tráfico aéreo. La posición de la aeronave se obtiene mediante multilateración, cuya técnica consiste en utilizar las medidas de TOA de un mismo mensaje ADS-B obtenidas en las distintas estaciones base. El objetivo es estimar la posición de cada aeronave con la mayor precisión posible. Para poder diseñar el sistema que permite alcanzar este objetivo, son dos los aspectos básicos a estudiar. Por una parte, la identificación y posterior caracterización de los errores (tanto sistemáticos como aleatorios) que afectan a la medida de TOA. Por otra parte, es necesario el estudio de los sistemas de seguimiento, basados en versiones sofisticadas del filtro de Kalman (IMM, UKF). Una vez establecidos estos dos pilares, la presente tesis doctoral propone un sistema que permite efectuar el seguimiento de las aeronaves, corrigiendo los efectos de las principales distorsiones que afectan a la medida de TOA: la refracción troposférica y el error de sincronismo. La mejora en la precisión de la localización ha sido evaluada mediante simulación de escenarios hipotéticos. ABSTRACT The ever-growing demand in the air transportation and the new military intervention scenarios, are generating a need to optimize the use of the airspace. This way, the EU and the USA (through SESAR and NextGen respectively) have set the ground to overhaul the current air traffic management. The intention is to enhance the capacity of airports and air routes, providing greater flexibility in the use of airspace without jeopardizing the security of the end-users. From a technical perspective, the key for this change lies in the knowledge of the aircraft position. The trend in Air Traffic Management (ATM) is to rely on ADS-B as the main source for aircraft positioning. However, this system is based on the aircraft’s self-declaration of its own (often GPS-based) navigation solution. It is therefore necessary to have an independent surveillance system. Nowadays, the intention is to gradually migrate from Secondary Surveillance Radar (SSR) towards Wide Area Multilateration (WAM) in order to enhance surveillance integrity for en-route applications. Given the fast deployment of ADS-B, the aim is to use its base stations for WAM. Each station sends the Time of Arrival (TOA) of the received ADS-B messages to the air traffic center (ATC). The aircraft position is obtained through multilateration, using the TOA of the same message measured by each station. The aim is to accurately estimate the position of each aircraft. Knowledge from two key areas has to be gathered prior to designing such a system. It is necessary to identify and then characterize the errors (both systematic and random) affecting the TOA measurements. The second element is the study of tracking systems based on sophisticated versions of the Kalman filtering (e.g. IMM, UKF). Based on this knowledge, the main contribution of this Ph.D. is an aircraft tracking system that corrects the effects of the main errors involved in the TOA measurement: tropospheric refraction and synchronization issues. Performance gains in positioning accuracy have been assessed by simulating hypothetical WAM scenarios.
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The challenge of persistent navigation and mapping is to develop an autonomous robot system that can simultaneously localize, map and navigate over the lifetime of the robot with little or no human intervention. Most solutions to the simultaneous localization and mapping (SLAM) problem aim to produce highly accurate maps of areas that are assumed to be static. In contrast, solutions for persistent navigation and mapping must produce reliable goal-directed navigation outcomes in an environment that is assumed to be in constant flux. We investigate the persistent navigation and mapping problem in the context of an autonomous robot that performs mock deliveries in a working office environment over a two-week period. The solution was based on the biologically inspired visual SLAM system, RatSLAM. RatSLAM performed SLAM continuously while interacting with global and local navigation systems, and a task selection module that selected between exploration, delivery, and recharging modes. The robot performed 1,143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), traveled a total distance of more than 40 km over 37 hours of active operation, and recharged autonomously a total of 23 times.
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The challenge of persistent appearance-based navigation and mapping is to develop an autonomous robotic vision system that can simultaneously localize, map and navigate over the lifetime of the robot. However, the computation time and memory requirements of current appearance-based methods typically scale not only with the size of the environment but also with the operation time of the platform; also, repeated revisits to locations will develop multiple competing representations which reduce recall performance. In this paper we present a solution to the persistent localization, mapping and global path planning problem in the context of a delivery robot in an office environment over a one-week period. Using a graphical appearance-based SLAM algorithm, CAT-Graph, we demonstrate constant time and memory loop closure detection with minimal degradation during repeated revisits to locations, along with topological path planning that improves over time without using a global metric representation. We compare the localization performance of CAT-Graph to openFABMAP, an appearance-only SLAM algorithm, and the path planning performance to occupancy-grid based metric SLAM. We discuss the limitations of the algorithm with regard to environment change over time and illustrate how the topological graph representation can be coupled with local movement behaviors for persistent autonomous robot navigation.
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The development of global navigation satellite systems (GNSS) provides a solution of many applied problems with increasingly higher quality and accuracy nowadays. Researches that are carried out by the Bavarian Academy of Sciences and Humanities in Munich (BAW) in the field of airborne gravimetry are based on sophisticated data processing from high frequency GNSS receiver for kinematic aircraft positioning. Applied algorithms for inertial acceleration determination are based on the high sampling rate (50Hz) and on reducing of such factors as ionosphere scintillation and multipath at aircraft /antenna near field effects. The quality of the GNSS derived kinematic height are studied also by intercomparison with lift height variations collected by a precise high sampling rate vertical scale [1]. This work is aimed at the ways of more accurate determination of mini-aircraft altitude by means of high frequency GNSS receivers, in particular by considering their dynamic behaviour.
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Acoustic recordings play an increasingly important role in monitoring terrestrial and aquatic environments. However, rapid advances in technology make it possible to accumulate thousands of hours of recordings, more than ecologists can ever listen to. Our approach to this big-data challenge is to visualize the content of long-duration audio recordings on multiple scales, from minutes, hours, days to years. The visualization should facilitate navigation and yield ecologically meaningful information prior to listening to the audio. To construct images, we calculate acoustic indices, statistics that describe the distribution of acoustic energy and reflect content of ecological interest. We combine various indices to produce false-color spectrogram images that reveal acoustic content and facilitate navigation. The technical challenge we investigate in this work is how to navigate recordings that are days or even months in duration. We introduce a method of zooming through multiple temporal scales, analogous to Google Maps. However, the “landscape” to be navigated is not geographical and not therefore intrinsically visual, but rather a graphical representation of the underlying audio. We describe solutions to navigating spectrograms that range over three orders of magnitude of temporal scale. We make three sets of observations: 1. We determine that at least ten intermediate scale steps are required to zoom over three orders of magnitude of temporal scale; 2. We determine that three different visual representations are required to cover the range of temporal scales; 3. We present a solution to the problem of maintaining visual continuity when stepping between different visual representations. Finally, we demonstrate the utility of the approach with four case studies.
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Soft engineering solutions are the current standard for addressing coastal erosion in the US. In South Carolina, beach nourishment from offshore sand deposits and navigation channels has mostly replaced construction of seawalls and groins, which were common occurrences in earlier decades. Soft engineering solutions typically provide a more natural product than hard solutions, and also eliminate negative impacts to adjacent areas which are often associated with hard solutions. A soft engineering solution which may be underutilized in certain areas is shoal manipulation. (PDF contains 4 pages)
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The vehicle navigation problem studied in Bell (2009) is revisited and a time-dependent reverse Hyperstar algorithm is presented. This minimises the expected time of arrival at the destination, and all intermediate nodes, where expectation is based on a pessimistic (or risk-averse) view of unknown link delays. This may also be regarded as a hyperpath version of the Chabini and Lan (2002) algorithm, which itself is a time-dependent A* algorithm. Links are assigned undelayed travel times and maximum delays, both of which are potentially functions of the time of arrival at the respective link. The driver seeks probabilities for link use that minimise his/her maximum exposure to delay on the approach to each node, leading to the determination of the pessimistic expected time of arrival. Since the context considered is vehicle navigation where the driver is not making repeated trips, the probability of link use may be interpreted as a measure of link attractiveness, so a link with a zero probability of use is unattractive while a link with a probability of use equal to one will have no attractive alternatives. A solution algorithm is presented and proven to solve the problem provided the node potentials are feasible and a FIFO condition applies for undelayed link travel times. The paper concludes with a numerical example.