An Evaluation of Deep Water Navigation Systems for Autonomous Underwater Vehicles * *This work has been supported by the NATO Allied Command Transformation (ACT), under the Collaborative Anti-Submarine Warfare Programme (CASW), by the Office of Naval Research Global under grant no. N62909-16-1-2095, and by the Collaborative Localisation and Navigation of Autonomous Underwater Vehicles (COOLAUV) Agreement between NATO STO CMRE and Università di Pisa (ISME node).

Navigation of Autonomous Underwater Vehicles (AUVs) remains a challenge due to the impossibility to use radio frequency signals and Global Positioning System (GPS). Navigation systems usually integrate different proprioceptive sensors to estimate the asset and the speed of the vehicle. In particular, the Doppler Velocity Log (DVL) is fundamental during the navigation to have an accurate estimate of the vehicle’s speed. This work addresses the enhancement of the navigation performance of an AUV through the development of a Deep Water Navigation Filter (DWNF). The DWNF is able to work in those scenarios where traditional navigation sensors show their limits: e.g., deep waters where DVL bottom lock cannot be achieved, or areas where the use of traditionally used static and dedicated beacons is incompatible with the mission requirements. The proposed approach exploits the concept of using a network of vehicles cooperatively supporting each other for their navigation. Several types of measurements coming from the different nodes (i.e. acoustic positioning system such as ship-mounted SSBL acoustic positioning system, USBL, range measurements from the different nodes) are fused in an Extended Kalman Filter framework with the odometry data. DWNF pushes forward the idea of using a network of robotic assets as a provider of navigation services allowing more flexible and robust operations of the deployed system. The approach has been tested at sea during several experiments. We report here results from DWNF running successfully in real-time on the NATO STO-Centre for Maritime Research and Experimentation (CMRE) vehicles during the Dynamic Mongoose’17 experimentation off the South coast of Iceland (June-July 2017).

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Wireless Remote Control for Underwater Vehicles

Journal of Marine Science and Engineering ◽

10.3390/jmse8100736 ◽

2020 ◽

Vol 8 (10) ◽

pp. 736

Author(s):

Filippo Campagnaro ◽

Alberto Signori ◽

Michele Zorzi

Keyword(s):

Autonomous Underwater Vehicles ◽

Underwater Vehicles ◽

Communication Link ◽

Strong Impact ◽

Underwater Sensor Networks ◽

Navigation Systems ◽

Underwater Communication ◽

Commercial Off The Shelf ◽

The Stability ◽

New Applications

Nowadays, the increasing availability of commercial off-the-shelf underwater acoustic and non-acoustic (e.g., optical and electromagnetic) modems that can be employed for both short-range broadband and long-range low-rate communication, the increasing level of autonomy of underwater vehicles, and the refinement of their underwater navigation systems pave the way for several new applications, such as data muling from underwater sensor networks and the transmission of real-time video streams underwater. In addition, these new developments inspired many companies to start designing hybrid wireless-driven underwater vehicles specifically tailored for off-shore operations and that are able to behave either as remotely operated vehicles (ROVs) or as autonomous underwater vehicles (AUVs), depending on both the type of mission they are required to perform and the limitations imposed by underwater communication channels. In this paper, we evaluate the actual quality of service (QoS) achievable with an underwater wireless-piloted vehicle, addressing the realistic aspects found in the underwater domain, first reviewing the current state-of-the-art of communication technologies and then proposing the list of application streams needed for control of the underwater vehicle, grouping them in different working modes according to the level of autonomy required by the off-shore mission. The proposed system is finally evaluated by employing the DESERT Underwater simulation framework by specifically analyzing the QoS that can be provided to each application stream when using a multimodal underwater communication system specifically designed to support different traffic-based QoSs. Both the analysis and the results show that changes in the underwater environment have a strong impact on the range and on the stability of the communication link.

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Determination of Flow Parameters of a Water Flow Around an AUV Body

Robotics ◽

10.3390/robotics8010005 ◽

2019 ◽

Vol 8 (1) ◽

pp. 5

Author(s):

Julian Hoth ◽

Wojciech Kowalczyk

Keyword(s):

Water Flow ◽

Autonomous Underwater Vehicles ◽

Pressure Sensors ◽

Underwater Vehicles ◽

Support Vector ◽

Navigation Systems ◽

Pressure Data ◽

Flow Parameters ◽

Wide Range ◽

The Given

Autonomous underwater vehicles (AUVs) have changed the way marine environment is surveyed, monitored and mapped. Autonomous underwater vehicles have a wide range of applications in research, military, and commercial settings. AUVs not only perform a given task but also adapt to changes in the environment, e.g., sudden side currents, downdrafts, and other effects which are extremely unpredictable. To navigate properly and allow simultaneous localisation and mapping (SLAM) algorithms to be used, these effects need to be detected. With current navigation systems, these disturbances in the water flow are not measured directly. Only the indirect effects are observed. It is proposed to detect the disturbances directly by placing pressure sensors on the surface of the AUV and processing the pressure data obtained. Within this study, the applicability of different learning methods for determining flow parameters of a surrounding fluid from pressure on an AUV body are tested. This is based on CFD simulations using pressure data from specified points on the surface of the AUV. It is shown that support vector machines are most suitable for the given task and yield excellent results.

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MOTION ESTIMATION OF AN UNDERWATER PLATFORM USING IMAGES FROM TWO SONAR SENSORS

10.36227/techrxiv.15059991 ◽

2021 ◽

Author(s):

José Enrique Almanza-Medina ◽

Benjamin Henson ◽

Yuriy Zakharov

Keyword(s):

Motion Estimation ◽

Performance Improvement ◽

Autonomous Underwater Vehicles ◽

Underwater Vehicles ◽

High Accuracy ◽

Estimation Accuracy ◽

Navigation Systems ◽

Sonar Sensors ◽

Acoustic Images ◽

Single Sensor

Many underwater applications that involve the use of autonomous underwater vehicles require accurate navigation systems. Image registration from acoustic images is a technique that can be used to achieve this task by comparing two consecutive sonar images and estimate the motion of the vechicle. The use of deep learning (DL) techniques for motion estimation can significantly reduce the processing complexity and achieve high-accuracy position estimates. In this paper we investigate the performance improvement when using two sonar sensors compared to using a single sensor. The DL network is trained using images generated by a sonar simulator. The results show an improvement in the estimation accuracy when using two sensors.

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Algorithm for dynamic formation of a single raster photo map of the seabed during the movement of an autonomous underwater vehicle

Journal of Physics Conference Series ◽

10.1088/1742-6596/2091/1/012054 ◽

2021 ◽

Vol 2091 (1) ◽

pp. 012054

Author(s):

A A Timoshenko ◽

A V Zuev ◽

E S Mursalimov

Keyword(s):

Autonomous Underwater Vehicle ◽

Autonomous Underwater Vehicles ◽

Real Data ◽

Underwater Vehicle ◽

Underwater Vehicles ◽

Navigation Systems ◽

Geometric Transformations ◽

Graphical Information ◽

Dynamic Formation

Abstract An algorithm has been developed for creating a single raster map of the seabed from photos obtained from vertically downward cameras of autonomous underwater vehicles (AUV) using tile graphics. The images obtained during the movement of AUV are combined into a single scalable photo map, divided into square segments (tiles). This representation of graphical information allows to quickly access the images with specialized tools after lifting the AUV to the surface and reduce the time spent by the operator to analyze the results of the mission. The images were combined using simple geometric transformations based on the data received from the navigation systems of the underwater vehicle and the parameters of its camera. The efficiency of the algorithm was tested on real data taken from a marine expedition.

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6-DoF Navigation Systems for Autonomous Underwater Vehicles

Mobile Robots Navigation ◽

10.5772/8978 ◽

2010 ◽

Cited By ~ 5

Author(s):

Andrew Lammas ◽

Karl Sammut ◽

Fangpo He

Keyword(s):

Autonomous Underwater Vehicles ◽

Underwater Vehicles ◽

Navigation Systems

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Multi-source information fusion based on factor graph in autonomous underwater vehicles navigation systems

Assembly Automation ◽

10.1108/aa-10-2020-0155 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Xiaoshuang Ma ◽

Xixiang Liu ◽

Chen-Long Li ◽

Shuangliang Che

Keyword(s):

Information Fusion ◽

Autonomous Underwater Vehicles ◽

Graph Model ◽

Underwater Vehicles ◽

Factor Graph ◽

Measurement Unit ◽

Navigation Systems ◽

Source Information ◽

Content Type ◽

Factor Graph Model

Purpose This paper aims to present a multi-source information fusion algorithm based on factor graph for autonomous underwater vehicles (AUVs) navigation and positioning to address the asynchronous and heterogeneous problem of multiple sensors. Design/methodology/approach The factor graph is formulated by joint probability distribution function (pdf) random variables. All available measurements are processed into an optimal navigation solution by the message passing algorithm in the factor graph model. To further aid high-rate navigation solutions, the equivalent inertial measurement unit (IMU) factor is introduced to replace several consecutive IMU measurements in the factor graph model. Findings The proposed factor graph was demonstrated both in a simulated and vehicle environment using IMU, Doppler Velocity Log, terrain-aided navigation, magnetic compass pilot and depth meter sensors. Simulation results showed that the proposed factor graph processes all available measurements into the considerably improved navigation performance, computational efficiency and complexity compared with the un-simplified factor graph and the federal Kalman filtering methods. Semi-physical experiment results also verified the robustness and effectiveness. Originality/value The proposed factor graph scheme supported a plug and play capability to easily fuse asynchronous heterogeneous measurements information in AUV navigation systems.

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MOTION ESTIMATION OF AN UNDERWATER PLATFORM USING IMAGES FROM TWO SONAR SENSORS

10.36227/techrxiv.15059991.v1 ◽

2021 ◽

Author(s):

José Enrique Almanza-Medina ◽

Benjamin Henson ◽

Yuriy Zakharov

Keyword(s):

Motion Estimation ◽

Performance Improvement ◽

Autonomous Underwater Vehicles ◽

Underwater Vehicles ◽

High Accuracy ◽

Estimation Accuracy ◽

Navigation Systems ◽

Sonar Sensors ◽

Acoustic Images ◽

Single Sensor

Many underwater applications that involve the use of autonomous underwater vehicles require accurate navigation systems. Image registration from acoustic images is a technique that can be used to achieve this task by comparing two consecutive sonar images and estimate the motion of the vechicle. The use of deep learning (DL) techniques for motion estimation can significantly reduce the processing complexity and achieve high-accuracy position estimates. In this paper we investigate the performance improvement when using two sonar sensors compared to using a single sensor. The DL network is trained using images generated by a sonar simulator. The results show an improvement in the estimation accuracy when using two sensors.

Download Full-text