An intelligent navigation system for an unmanned surface vehicle

Merged with duplicate record 10026.1/2768 on 27.03.2017 by CS (TIS)A multi-disciplinary research project has been carried out at the University of Plymouth to design and develop an Unmanned Surface Vehicle (USV) named ýpringer. The work presented herein relates to formulation of a robust, reliable, accurate and adaptable navigation system to enable opringei to undertake various environmental monitoring tasks. Synergistically, sensor mathematical modelling, fuzzy logic, Multi-Sensor Data Fusion (MSDF), Multi-Model Adaptive Estimation (MMAE), fault adaptive data acquisition and an user interface system are combined to enhance the robustness and fault tolerance of the onboard navigation system. This thesis not only provides a holistic framework but also a concourse of computational techniques in the design of a fault tolerant navigation system. One of the principle novelties of this research is the use of various fuzzy logic based MSDF algorithms to provide an adaptive heading angle under various fault situations for Springer. This algorithm adapts the process noise covariance matrix ( Q) and measurement noise covariance matrix (R) in order to address one of the disadvantages of Kalman filtering. This algorithm has been implemented in Spi-inger in real time and results demonstrate excellent robustness qualities. In addition to the fuzzy logic based MSDF, a unique MMAE algorithm has been proposed in order to provide an alternative approach to enhance the fault tolerance of the heading angles for Springer. To the author's knowledge, the work presented in this thesis suggests a novel way forward in the development of autonomous navigation system design and, therefore, it is considered that the work constitutes a contribution to knowledge in this area of study. Also, there are a number of ways in which the work presented in this thesis can be extended to many other challenging domains.DEVONPORT MANAGEMENT LTD, J&S MARINE LTD AND SOUTH WEST WATER PL

AN INTELLIGENT NAVIGATION SYSTEM FOR AN AUTONOMOUS UNDERWATER VEHICLE

The work in this thesis concerns with the development of a novel multisensor data fusion (MSDF) technique, which combines synergistically Kalman filtering, fuzzy logic and genetic algorithm approaches, aimed to enhance the accuracy of an autonomous underwater vehicle (AUV) navigation system, formed by an integration of global positioning system and inertial navigation system (GPS/INS). The Kalman filter has been a popular method for integrating the data produced by the GPS and INS to provide optimal estimates of AUVs position and attitude. In this thesis, a sequential use of a linear Kalman filter and extended Kalman filter is proposed. The former is used to fuse the data from a variety of INS sensors whose output is used as an input to the later where integration with GPS data takes place. The use of an adaptation scheme based on fuzzy logic approaches to cope with the divergence problem caused by the insufficiently known a priori filter statistics is also explored. The choice of fuzzy membership functions for the adaptation scheme is first carried out using a heuristic approach. Single objective and multiobjective genetic algorithm techniques are then used to optimize the parameters of the membership functions with respect to a certain performance criteria in order to improve the overall accuracy of the integrated navigation system. Results are presented that show that the proposed algorithms can provide a significant improvement in the overall navigation performance of an autonomous underwater vehicle navigation. The proposed technique is known to be the first method used in relation to AUV navigation technology and is thus considered as a major contribution thereof.J&S Marine Ltd., Qinetiq, Subsea 7 and South West Water PL

Robust Multi-sensor Data Fusion for Practical Unmanned Surface Vehicles (USVs) Navigation

The development of practical Unmanned Surface Vehicles (USVs) are attracting increasing attention driven by their assorted military and commercial application potential. However, addressing the uncertainties presented in practical navigational sensor measurements of an USV in maritime environment remain the main challenge of the development. This research aims to develop a multi-sensor data fusion system to autonomously provide an USV reliable navigational information on its own positions and headings as well as to detect dynamic target ships in the surrounding environment in a holistic fashion. A multi-sensor data fusion algorithm based on Unscented Kalman Filter (UKF) has been developed to generate more accurate estimations of USV’s navigational data considering practical environmental disturbances. A novel covariance matching adaptive estimation algorithm has been proposed to deal with the issues caused by unknown and varying sensor noise in practice to improve system robustness. Certain measures have been designed to determine the system reliability numerically, to recover USV trajectory during short term sensor signal loss, and to autonomously detect and discard permanently malfunctioned sensors, and thereby enabling potential sensor faults tolerance. The performance of the algorithms have been assessed by carrying out theoretical simulations as well as using experimental data collected from a real-world USV projected collaborated with Plymouth University. To increase the degree of autonomy of USVs in perceiving surrounding environments, target detection and prediction algorithms using an Automatic Identification System (AIS) in conjunction with a marine radar have been proposed to provide full detections of multiple dynamic targets in a wider coverage range, remedying the narrow detection range and sensor uncertainties of the AIS. The detection algorithms have been validated in simulations using practical environments with water current effects. The performance of developed multi-senor data fusion system in providing reliable navigational data and perceiving surrounding environment for USV navigation have been comprehensively demonstrated

Low-cost RPAS navigation and guidance system using Square Root Unscented Kalman Filter

Multi-Sensor Data Fusion (MSDF) techniques involving satellite and inertial-based sensors are widely adopted to improve the navigation solution of a number of mission- and safety-critical tasks. Such integrated Navigation and Guidance Systems (NGS) currently do not meet the required level of performance in all flight phases of small Remotely Piloted Aircraft Systems (RPAS). In this paper an innovative Square Root-Unscented Kalman Filter (SR-UKF) based NGS is presented and compared with a conventional UKF governed design. The presented system architectures adopt state-of-the-art information fusion approach based on a number of low-cost sensors including; Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Navigation (VBN) sensors. Additionally, an Aircraft Dynamics Model (ADM), which is essentially a knowledge based module, is employed to compensate for the MEMS-IMU sensor shortcomings in high-dynamics attitude determination tasks. The ADM acts as a virtual sensor and its measurements are processed with non-linear estimation in order to increase the operational validity time. An improvement in the ADM navigation state vector (i.e., position, velocity and attitude) measurements is obtained, thanks to the accurate modeling of aircraft dynamics and advanced processing techniques. An innovative SR-UKF based VBN-IMU-GNSS-ADM (SR-U-VIGA) architecture design was implemented and compared with a typical UKF design (U-VIGA) in a small RPAS (AEROSONDE) integration arrangement exploring a representative cross-section of the operational flight envelope. The comparison of position and attitude data shows that the SR-U-VIGA and U-VIGA NGS fulfill the relevant RNP criteria, including precision approach tasks

Vehicle positioning in urban environments using particle filtering-based global positioning system, odometry, and map data fusion

This article presents a new method for land vehicle navigation using global positioning system (GPS), dead reckoning sensor (DR), and digital road map information, particularly in urban environments where GPS failures can occur. The odometer sensors and map measure can be used to provide continuous navigation and correct the vehicle location in the presence of GPS masking. To solve this estimation problem for vehicle navigation, we propose to use particle filtering for GPS/odometer/map integration. The particle filter is a method based on the Bayesian estimation technique and the Monte Carlo method, which deals with non-linear models and is not limited to Gaussian statistics. When the GPS sensor cannot provide a location due to the number of satellites in view, the filter fuses the limited GPS pseudo-range data to enhance the vehicle positioning. The developed filter is then tested in a transportation network scenario in the presence of GPS failures, which shows the advantages of the proposed approach for vehicle location compared to the extended Kalman filter

Expanding Navigation Systems by Integrating It with Advanced Technologies

Navigation systems provide the optimized route from one location to another. It is mainly assisted by external technologies such as Global Positioning System (GPS) and satellite-based radio navigation systems. GPS has many advantages such as high accuracy, available anywhere, reliable, and self-calibrated. However, GPS is limited to outdoor operations. The practice of combining different sources of data to improve the overall outcome is commonly used in various domains. GIS is already integrated with GPS to provide the visualization and realization aspects of a given location. Internet of things (IoT) is a growing domain, where embedded sensors are connected to the Internet and so IoT improves existing navigation systems and expands its capabilities. This chapter proposes a framework based on the integration of GPS, GIS, IoT, and mobile communications to provide a comprehensive and accurate navigation solution. In the next section, we outline the limitations of GPS, and then we describe the integration of GIS, smartphones, and GPS to enable its use in mobile applications. For the rest of this chapter, we introduce various navigation implementations using alternate technologies integrated with GPS or operated as standalone devices

Tahap penguasaan, sikap dan minat pelajar Kolej Kemahiran Tinggi MARA terhadap mata pelajaran Bahasa Inggeris

Kajian ini dilakukan untuk mengenal pasti tahap penguasaan, sikap dan minat pelajar Kolej Kemahiran Tinggi Mara Sri Gading terhadap Bahasa Inggeris. Kajian yang dijalankan ini berbentuk deskriptif atau lebih dikenali sebagai kaedah tinjauan. Seramai 325 orang pelajar Diploma in Construction Technology dari Kolej Kemahiran Tinggi Mara di daerah Batu Pahat telah dipilih sebagai sampel dalam kajian ini. Data yang diperoleh melalui instrument soal selidik telah dianalisis untuk mendapatkan pengukuran min, sisihan piawai, dan Pekali Korelasi Pearson untuk melihat hubungan hasil dapatan data. Manakala, frekuensi dan peratusan digunakan bagi mengukur penguasaan pelajar. Hasil dapatan kajian menunjukkan bahawa tahap penguasaan Bahasa Inggeris pelajar adalah berada pada tahap sederhana manakala faktor utama yang mempengaruhi penguasaan Bahasa Inggeris tersebut adalah minat diikuti oleh sikap. Hasil dapatan menggunakan pekali Korelasi Pearson juga menunjukkan bahawa terdapat hubungan yang signifikan antara sikap dengan penguasaan Bahasa Inggeris dan antara minat dengan penguasaan Bahasa Inggeris. Kajian menunjukkan bahawa semakin positif sikap dan minat pelajar terhadap pengajaran dan pembelajaran Bahasa Inggeris semakin tinggi pencapaian mereka. Hasil daripada kajian ini diharapkan dapat membantu pelajar dalam meningkatkan penguasaan Bahasa Inggeris dengan memupuk sikap positif dalam diri serta meningkatkan minat mereka terhadap Bahasa Inggeris dengan lebih baik. Oleh itu, diharap kajian ini dapat memberi panduan kepada pihak-pihak yang terlibat dalam membuat kajian yang akan datang

Theoretical framework for In-Car Navigation based on Integrated GPS/IMU Technologies

In this report the problem of vehicular navigation based on the integration of the global positioning system and an inertial navigation system is tackled. After analysing some fundamental technical issues about reference systems, vehicle modelling and sensors, a novel solution, combining extended Kalman filtering with particle filltering, is developed. This solution allows to embed highly non-linear constraints originating from digital maps in the position estimation process and is expected to be implementable on commercial hardware platforms equipped with low cost inertial sensorsJRC.G.6-Digital Citizen Securit