An Underwater Vehicle Navigation System Using Acoustic and Inertial Sensors

Unmanned Underwater Vehicles (UUVs) have become an essential tool for different underwater tasks. Compared with other unmanned systems, the navigation and localization for UUVs are particularly challenging due to the unavailability of Global Positioning System (GPS) signals underwater and the complexity of the unstable environment. Alternative methods such as acoustic positioning systems, Inertial Navigation Systems (INS), and the geophysical navigation approach are used for UUV navigation. Acoustic positioning systems utilize the characteristics of acoustic signals that have a lower absorption rate and a more extended propagation distance than electromagnetic signals underwater. The significant disadvantage of the INS is the “drift,” the unbounded error growth over time in the outputs. This thesis is aimed to study and test a combined UUV navigation system that fuses measurements from the INS, Doppler Velocity Log (DVL), and Short Baseline (SBL) acoustic positioning system to reduce the drift. Two Kalman filters are used to do the fusion: the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). After conducting the experiments and simulation, the results illustrated the INS/SBL fusion navigation approach was able to reduce the drift problems in the INS. Moreover, UKF showed a better performance than the EKF in the INS

Simultaneous Mapping and Localisation for Small Military Unmanned Underwater Vehicle

Paper proposes a simultaneous localisation and mapping (SLAM) scheme which is applicable to small military unmanned underwater vehicles (UUVs). The SLAM is a process which enables concurrent estimation of the position of UUV and landmarks in the environment through which the vehicle is passing. An unscented Kalman filter (UKF) is utilised to develop a SLAM suitable to nonlinear motion of UUV. A range sonar is used as a sensor to collect the relative position information of the landmark in the environment in which the UUV is navigating. The proposed SLAM scheme was validated through towing tank experiments about two degrees of freedom motion with UUV motion simulator and real range sonar system for small UUV. The results of these experiments showed that proposed SLAM scheme is capable of estimating the position of the UUV and the surrounding objects under real underwater environment.Defence Science Journal, 2012, 62(4), pp.223-227, DOI:http://dx.doi.org/10.14429/dsj.62.100

An unscented Kalman filter based navigation algorithm for autonomous underwater vehicles

Robust and performing navigation systems for Autonomous Underwater Vehicles (AUVs) play a discriminant role towards the success of complex underwater missions involving one or more AUVs. The quality of the filtering algorithm for the estimation of the AUV navigation state strongly affects the performance of the overall system. In this paper, the authors present a comparison between the Extended Kalman Filter (EKF) approach, classically used in the field of underwater robotics and an Unscented Kalman Filter (UKF). The comparison results to be significant as the two strategies of filtering are based on the same process and sensors models. The UKF-based approach, here adapted to the AUV case, demonstrates to be a good trade-off between estimation accuracy and computational load. UKF has not yet been extensively used in practical underwater applications, even if it turns out to be quite promising. The proposed results rely on the data acquired during a sea mission performed by one of the two Typhoon class vehicles involved in the NATO CommsNet13 experiment (held in September 2013). As ground truth for performance evaluation and comparison, performed offline, position measurements obtained through Ultra-Short BaseLine (USBL) fixes are used. The result analysis leads to identify both the strategies as effective for the purpose of being included in the control loop of an AUV. The UKF approach demonstrates higher performance encouraging its implementation as a more suitable navigation algorithm even if, up to now, it is still not used much in this field

Development of a navigation algorithm for autonomous underwater vehicles

In this paper, the authors present an underwater navigation system for Autonomous Underwater Vehicles (AUVs) which exploits measurements from an Inertial Measurement Unit (IMU), a Pressure Sensor (PS) for depth and the Global Positioning System (GPS, used during periodic and dedicated resurfacings) and relies on either the Extended Kalman Filter (EKF) or the Unscented Kalman Filter (UKF) for the state estimation. Both (EKF and UKF) navigation algorithms have been validated through experimental navigation data related to some sea tests performed in La Spezia (Italy) with one of Typhoon class vehicles during the NATO CommsNet13 experiment (held in September 2013) and through Ultra-Short BaseLine (USBL) fixes used as a reference (ground truth). Typhoon is an AUV designed by the Department of Industrial Engineering of the Florence University for exploration and surveillance of underwater archaeological sites in the framework of the Italian THESAURUS project and the European ARROWS project. The obtained results have demonstrated the effectiveness of both navigation algorithms and the superiority of the UKF (very suitable for AUV navigation and, up to now, still not used much in this field) without increasing the computational load (affordable for on-line on-board AUV implementation)

Towards autonomous localization and mapping of AUVs: a survey

Purpose The main purpose of this paper is to investigate two key elements of localization and mapping of Autonomous Underwater Vehicle (AUV), i.e. to overview various sensors and algorithms used for underwater localization and mapping, and to make suggestions for future research. Design/methodology/approach The authors first review various sensors and algorithms used for AUVs in the terms of basic working principle, characters, their advantages and disadvantages. The statistical analysis is carried out by studying 35 AUV platforms according to the application circumstances of sensors and algorithms. Findings As real-world applications have different requirements and specifications, it is necessary to select the most appropriate one by balancing various factors such as accuracy, cost, size, etc. Although highly accurate localization and mapping in an underwater environment is very difficult, more and more accurate and robust navigation solutions will be achieved with the development of both sensors and algorithms. Research limitations/implications This paper provides an overview of the state of art underwater localisation and mapping algorithms and systems. No experiments are conducted for verification. Practical implications The paper will give readers a clear guideline to find suitable underwater localisation and mapping algorithms and systems for their practical applications in hand. Social implications There is a wide range of audiences who will benefit from reading this comprehensive survey of autonomous localisation and mapping of UAVs. Originality/value The paper will provide useful information and suggestions to research students, engineers and scientists who work in the field of autonomous underwater vehicles

CES-515 Towards Localization and Mapping of Autonomous Underwater Vehicles: A Survey

Autonomous Underwater Vehicles (AUVs) have been used for a huge number of tasks ranging from commercial, military and research areas etc, while the fundamental function of a successful AUV is its localization and mapping ability. This report aims to review the relevant elements of localization and mapping for AUVs. First, a brief introduction of the concept and the historical development of AUVs is given; then a relatively detailed description of the sensor system used for AUV navigation is provided. As the main part of the report, a comprehensive investigation of the simultaneous localization and mapping (SLAM) for AUVs are conducted, including its application examples. Finally a brief conclusion is summarized

6-DoF Navigation Systems for Autonomous Underwater Vehicles

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AN INFORMATION THEORETIC APPROACH TO INTERACTING MULTIPLE MODEL ESTIMATION FOR AUTONOMOUS UNDERWATER VEHICLES

Accurate and robust autonomous underwater navigation (AUV) requires the fundamental task of position estimation in a variety of conditions. Additionally, the U.S. Navy would prefer to have systems that are not dependent on external beacon systems such as global positioning system (GPS), since they are subject to jamming and spoofing and can reduce operational effectiveness. Current methodologies such as Terrain-Aided Navigation (TAN) use exteroceptive imaging sensors for building a local reference position estimate and will not be useful when those sensors are out of range. What is needed are multiple navigation filters where each can be more effective depending on the mission conditions. This thesis investigates how to combine multiple navigation filters to provide a more robust AUV position estimate. The solution presented is to blend two different filtering methodologies utilizing an interacting multiple model (IMM) estimation approach based on an information theoretic framework. The first filter is a model-based Extended Kalman Filter (EKF) that is effective under dead reckoning (DR) conditions. The second is a Particle Filter approach for Active Terrain Aided Navigation (ATAN) that is appropriate when in sensor range. Using data collected at Lake Crescent, Washington, each of the navigation filters are developed with results and then we demonstrate how an IMM information theoretic approach can be used to blend approaches to improve position and orientation estimation.Lieutenant, United States NavyApproved for public release. Distribution is unlimited