Application of Sequential Quasi-Monte Carlo to Autonomous Positioning

Sequential Monte Carlo algorithms (also known as particle filters) are popular methods to approximate filtering (and related) distributions of state-space models. However, they converge at the slow $1/\sqrt{N}$ rate, which may be an issue in real-time data-intensive scenarios. We give a brief outline of SQMC (Sequential Quasi-Monte Carlo), a variant of SMC based on low-discrepancy point sets proposed by Gerber and Chopin (2015), which converges at a faster rate, and we illustrate the greater performance of SQMC on autonomous positioning problems.Comment: 5 pages, 4 figure

Particle filtering with soft state constraints for target tracking

In practice, additional knowledge about the target to be tracked, other than its fundamental dynamics, can often be modelled as a set of soft constraints and utilised in a filtering process to improve the tracking performance. This paper develops a general approach to the modelling of soft inequality constraints, and investigates particle filtering with soft state constraints for target tracking. We develop two particle filtering algorithms with soft inequality constraints, i.e. a sequential-importanceresampling particle filter and an auxiliary sampling mechanism. The latter probabilistically selects the candidate particles from the soft inequality constraints of the state variables so that they are more likely to comply with the soft constraints. The performances of the proposed algorithms are evaluated using Monte Carlo simulations in a target tracking scenario

On Fault Detection and Exclusion in Snapshot and Recursive Positioning Algorithms for Maritime Applications

Resilient provision of Position, Navigation and Timing (PNT) data can be considered as a key element of the e-Navigation strategy developed by the International Maritime Organization (IMO). An indication of reliability has been identified as a high level user need with respect to PNT data to be supplied by electronic navigation means. The paper concentrates on the Fault Detection and Exclusion (FDE) component of the Integrity Monitoring (IM) for navigation systems based both on pure GNSS (Global Navigation Satellite Systems) as well as on hybrid GNSS/inertial measurements. Here a PNT-data processing Unit will be responsible for both the integration of data provided by all available on-board sensors as well as for the IM functionality. The IM mechanism can be seen as an instantaneous decision criterion for using or not using the system and, therefore, constitutes a key component within a process of provision of reliable navigational data in future navigation systems. The performance of the FDE functionality is demonstrated for a pure GNSS-based snapshot weighted iterative least-square (WLS) solution, a GNSS-based Extended Kalman Filter (EKF) as well as for a classical error-state tightly-coupled EKF for the hybrid GNSS/inertial system. Pure GNSS approaches are evaluated by combining true measurement data collected in port operation scenario with artificially induced measurement faults, while for the hybrid navigation system the measurement data in an open sea scenario with native GNSS measurement faults have been employed. The work confirms the general superiority of the recursive Bayesian scheme with FDE over the snapshot algorithms in terms of fault detection performance even for the case of GNSS-only navigation. Finally, the work demonstrates a clear improvement of the FDE schemes over non-FDE approaches when the FDE functionality is implemented within a hybrid integrated navigation system

A Real-Time Adaptive Sampling Strategy Optimized by Uncertainty for Spatial Data Analysis on Complex Domains

Environmental monitoring is used to reveal the state of the environment, to inform experts and help them to prioritise actions in the context of environmental policies. Environmental sampling is the way the environment is interrogated to get measures of environmental (e.g., physical, chemical) parameters in a limited set of locations (samples). The environmental properties varies from place to place in continuum and there are infinitely many places at which we might record what they are like, but practically we can measure them at only a finite number by sampling. The role of the location in which samples are collected is very crucial. The focus of the thesis is the study of a mathematical framework that supports a reasoned and non-random sampling of environmental variables, with the aim of defining a methodological approach to optimise the number of sampling required while maintaining a target precision. The arrangement of points is not selected or known a priori; conversely, we propose an iterative process where the next-sample location is determined on-the-fly on the basis of the environmental scenario that is delineated more and more accurately at each iteration. At each iteration, the distribution map is updated with the new incoming data. The geostatistical analysis we implement provides a predicted value and the related uncertainty about that value, actually providing an uncertainty map beside the predicted distribution. The system responds to the current state by requiring a measurement in the area with highest uncertainty, to reduce uncertainty and increase accuracy. Environmental survey areas to monitor are often characterised by very complex boundaries. Unstructured grids are more flexible to faithfully represent complex geometries compared to structured grids. The usage of unstructured grids introduces another innovation aspect studied in the thesis, which is the change of support model

Autonomous Simultaneous Localization and Mapping driven by Monte Carlo uncertainty maps-based navigation

This paper addresses the problem of implementing a Simultaneous Localization and Mapping (SLAM) algorithm combined with a non-reactive controller (such as trajectory following or path following). A general study showing the advantages of using predictors to avoid mapping inconsistences in autonomous SLAM architectures is presented. In addition, this paper presents a priority-based uncertainty map construction method of the environment by a mobile robot when executing a SLAM algorithm. The SLAM algorithm is implemented with an extended Kalman filter (EKF) and extracts corners (convex and concave) and lines (associated with walls) from the surrounding environment. A navigation approach directs the robot motion to the regions of the environment with the higher uncertainty and the higher priority. The uncertainty of a region is specified by a probability characterization computed at the corresponding representative points. These points are obtained by a Monte Carlo experiment and their probability is estimated by the sum of Gaussians method, avoiding the time-consuming map-gridding procedure. The priority is determined by the frame in which the uncertainty region was detected (either local or global to the vehicle's pose). The mobile robot has a non-reactive trajectory following controller implemented on it to drive the vehicle to the uncertainty points. SLAM real-time experiments in real environment, navigation examples, uncertainty maps constructions along with algorithm strategies and architectures are also included in this work.Fil: Auat Cheein, Fernando Alfredo. Universidad Técnica Federico Santa María; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pereira, Fernando M. Lobo. Universidad de Porto; PortugalFil: Di Sciascio, Fernando Agustín. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

Research on port AGV composite positioning based on UWB/RFID

In recent years, ports in various countries have successively carried out research and application of fully automated terminal. The terminal adopts the "Double car shore bridge + AGV + ARMG" automation process, which is the most widely used and relatively mature fully automated solution. At present, the AGV navigation of the terminal is based on RFID magnetic nail positioning and the accuracy is good. However, nowadays UWB technology has become the most popular technology in ranging and positioning. The research in this work is based on UWB/RFID composite positioning, which is mainly used for the specific localization tasks in the port and it can accurately locate the position of the AGV. This MSc work studies the UWB positioning system first and then researches the traditional 3D positioning algorithm. Importance contribution expressed by 3D TOA localization algorithm. For RFID system, this connection between the reader and the carrier is designed, and the reference tag is buried. At last, data-based on RFID localization algorithm in scene analysis method is adopted for positioning. Secondly, the basis of the composite positioning system is data fusion technology. The most widely used and mature fusion algorithm is the Kalman filter algorithm and Particle filter. Finally, the experimental analysis of UWB and RFID composite positioning system is implemented. The results indicate that UWB and RFID composite positioning system can reduce the cost of the positioning system. Higher positioning accuracy and robustness are characterizing the developed system.Nos últimos anos, portos de vários países realizaram sucessivamente pesquisas e aplicações de terminais totalmente automatizados. O terminal adota o processo de automação "Double car shore bridge + AGV + ARMG", que é a solução totalmente automatizada mais amplamente utilizada e relativamente madura. Atualmente, a navegação AGV do terminal é baseada no posicionamento da etiqueta RFID e a precisão é boa. No entanto, hoje em dia, a tecnologia UWB tornou-se na tecnologia mais popular relativamente ao alcance e posicionamento. A pesquisa neste trabalho é baseada no posicionamento composto por UWB / RFID, usado principalmente para tarefas de localização específicas nos portos, podendo desta forma localizar-se com precisão a posição do AGV. Este projeto de mestrado estuda em primeiro lugar o sistema de posicionamento UWB, e depois um algoritmo tradicional de posicionamento 3D. A contribuição da importância expressa pelo algoritmo de posicionamento “time of arrival” (TOA) 3D foi proposta. Para o sistema de posicionamento RFID, a conexão entre o leitor e a transportadora é projetada e a etiqueta de referência é ocultada. Por fim, o algoritmo de “k-nearest neighbor” baseado numa base de dados e no método de análise de cena é adotado para realizar o posicionamento. Em segundo lugar, a base do sistema de posicionamento composto é a tecnologia de fusão de dados. O algoritmo de fusão mais amplamente utilizado e maduro é o algoritmo de filtro Kalman e o filtro de partículas. Finalmente, é realizada a análise experimental do sistema de posicionamento composto UWB e RFID. Os resultados experimentais mostram que o sistema de posicionamento composto UWB e RFID pode reduzir o custo do sistema de posicionamento. O sistema desenvolvido é caracterizado por uma maior precisão de posicionamento e robustez

Unmanned Aircraft System Navigation in the Urban Environment: A Systems Analysis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140665/1/1.I010280.pd

Quaternionic Attitude Estimation with Inertial Measuring Unit for Robotic and Human Body Motion Tracking using Sequential Monte Carlo Methods with Hyper-Dimensional Spherical Distributions

This dissertation examined the inertial tracking technology for robotics and human tracking applications. This is a multi-discipline research that builds on the embedded system engineering, Bayesian estimation theory, software engineering, directional statistics, and biomedical engineering. A discussion of the orientation tracking representations and fundamentals of attitude estimation are presented briefly to outline the some of the issues in each approach. In addition, a discussion regarding to inertial tracking sensors gives an insight to the basic science and limitations in each of the sensing components. An initial experiment was conducted with existing inertial tracker to study the feasibility of using this technology in human motion tracking. Several areas of improvement were made based on the results and analyses from the experiment. As the performance of the system relies on multiple factors from different disciplines, the only viable solution is to optimize the performance in each area. Hence, a top-down approach was used in developing this system. The implementations of the new generation of hardware system design and firmware structure are presented in this dissertation. The calibration of the system, which is one of the most important factors to minimize the estimation error to the system, is also discussed in details. A practical approach using sequential Monte Carlo method with hyper-dimensional statistical geometry is taken to develop the algorithm for recursive estimation with quaternions. An analysis conducted from a simulation study provides insights to the capability of the new algorithms. An extensive testing and experiments was conducted with robotic manipulator and free hand human motion to demonstrate the improvements with the new generation of inertial tracker and the accuracy and stability of the algorithm. In addition, the tracking unit is used to demonstrate the potential in multiple biomedical applications including kinematics tracking and diagnosis instrumentation. The inertial tracking technologies presented in this dissertation is aimed to use specifically for human motion tracking. The goal is to integrate this technology into the next generation of medical diagnostic system