scholarly journals 6-DoF Navigation Systems for Autonomous Underwater Vehicles

Author(s):  
Andrew Lammas ◽  
Karl Sammut ◽  
Fangpo He
2020 ◽  
Vol 8 (10) ◽  
pp. 736
Author(s):  
Filippo Campagnaro ◽  
Alberto Signori ◽  
Michele Zorzi

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.


Robotics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 5
Author(s):  
Julian Hoth ◽  
Wojciech Kowalczyk

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.


2021 ◽  
Author(s):  
José Enrique Almanza-Medina ◽  
Benjamin Henson ◽  
Yuriy Zakharov

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.


2021 ◽  
Vol 2091 (1) ◽  
pp. 012054
Author(s):  
A A Timoshenko ◽  
A V Zuev ◽  
E S Mursalimov

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.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaoshuang Ma ◽  
Xixiang Liu ◽  
Chen-Long Li ◽  
Shuangliang Che

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.


2021 ◽  
Author(s):  
José Enrique Almanza-Medina ◽  
Benjamin Henson ◽  
Yuriy Zakharov

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.


2021 ◽  
Vol 2096 (1) ◽  
pp. 012146
Author(s):  
A A Timoshenko ◽  
A V Zuev ◽  
E S Mursalimov

Abstract An algorithm has been developed for creating one whole raster photo map of the seabed from images obtained from vertically downward cameras of autonomous underwater vehicles (AUV) using tile graphics. Tile representation of graphical information allows to quickly access the images after lifting the AUV to the surface and reduce the time spent by the operator to analyse the results of the mission. The images were combined using simple geometric transformations based on the data received from the navigation systems of the AUV and the parameters of its camera, so the algorithm can be implemented on the AUV with low-performance onboard computer, as shown in the experiment.


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