AUV/ASC cooperative survey

In this paper we describe a solution to perform autonomous surveys taking advantage of a cooperative multivehicle setup. In the proposed configuration, an ASC provides –through an USBL- absolute positioning and communications to an AUV. Thus, by following the AUV with the surface vehicle we facilitate the reception of USBL measurements in the AUV regardless of the extent of the mission. This turns into an improved navigation on the AUV’s side, with the drift bounded thanks to the absolute measurements. Experimental results show that the proposed algorithm is able to maintain the ASC at a close distance and improve the navigation of the AUV. Moreover, the bathymetric maps built from the AUV data are consistent enough to enable the automatic detection of present targets and program further localized missions in the area.Peer Reviewe

Depth control of autonomous underwater vehicle using discrete time sliding mode controller

This study presents a Discrete Time Sliding Mode Controller (DSMC) application on depth plane of Autonomous Underwater Vehicle (AUV). The main contribution on this work is an implementation of DSMC on NSP AUV II. Sliding Mode Control (SMC) is a robust type of controller and certainly suitable for controlling AUV in the presence of environmental disturbances and uncertainties. DSMC preserves the properties of standard SMC. Linearized dynamic model of NSP AUV II is used in the numerical simulations. Discrete Proportional Integral Derivative (PID) controllers are used for performance comparative analysis. The design of discrete PID and DSMC for NSP AUV II depth is described. Comparative study between the control laws is presented. The simulated results illustrate strong robustness, improve performance and satisfactory stability of DSMC as compared to discrete-time PID controller

Experimental Verification of a Depth Controller using Model Predictive Control with Constraints onboard a Thruster Actuated AUV

In this work a depth and pitch controller for an autonomous underwater vehicle (AUV) is developed. This controller uses the model predictive control method to manoeuvre the vehicle whilst operating within the defined constraints of the AUV actuators. Experimental results are given for the AUV performing a step change in depth whilst maintaining zero pitch

Correction of Bathymetric Survey Artifacts Resulting from Apparent Wave-Induced Vertical Position of an AUV

Recent increases in the capability and reliability of autonomous underwater vehicles (AUVs) have provided the opportunity to conduct bathymetric seafloor surveys in shallow water (\u3c 50 m). Unfortunately, surveys of this water depth may contain artifacts induced by large amplitude wave motion at the surface. The artifacts occur when an onboard pressure sensor determines the depth of the AUV. Waves overhead induce small pressure fluctuations at depth, which modulate the AUV’s pressure sensor output without causing actual vertical movement of the AUV. Since bathymetric measurements are made with respect to the AUV’s depth, these pressure fluctuations, in turn, modulate the measurement of the seafloor. The result is a periodic across-track, vertical offset of the seafloor profile (similar to a heave artifact sometimes common in surface vessel surveys). In this paper we describe our experience with the “Gavia” model AUV (Hafmynd EHF, Iceland) in a recent bathymetric survey during which wave action overhead induced such an artifact with a peak-to-peak amplitude as large as 1 meter. A method for removing the artifact as well as recommendations for modifications to the sonar, INS and AUV to mitigate the effect in the future are provided

Improving the energy efficiency of autonomous underwater vehicles by learning to model disturbances

Energy efficiency is one of the main challenges for long-term autonomy of AUVs (Autonomous Underwater Vehicles). We propose a novel approach for improving the energy efficiency of AUV controllers based on the ability to learn which external disturbances can safely be ignored. The proposed learning approach uses adaptive oscillators that are able to learn online the frequency, amplitude and phase of zero-mean periodic external disturbances. Such disturbances occur naturally in open water due to waves, currents, and gravity, but also can be caused by the dynamics and hydrodynamics of the AUV itself. We formulate the theoretical basis of the approach, and demonstrate its abilities on a number of input signals. Further experimental evaluation is conducted using a dynamic model of the Girona 500 AUV in simulation on two important underwater scenarios: hovering and trajectory tracking. The proposed approach shows significant energy-saving capabilities while at the same time maintaining high controller gains. The approach is generic and applicable not only for AUV control, but also for other type of control where periodic disturbances exist and could be accounted for by the controller. © 2013 IEEE

Uncertainty Modeling for AUV Acquired Bathymetry

Abstract Autonomous Underwater Vehicles (AUVs) are used across a wide range of mission scenarios and from an increasingly diverse set of operators. Use of AUVs for shallow water (less than 200 meters) mapping applications is of increasing interest. However, an update of the total propagated uncertainty TPU model is required to properly attribute bathymetry data acquired from an AUV platform compared with surface platform acquired data. An overview of the parameters that should be considered for data acquired from an AUV platform is discussed. Data acquired in August 2014 using NOAA’s Remote Environmental Measuring UnitS (REMUS) 600 AUV in the vicinity of Portsmouth, NH were processed and analyzed through Leidos’ Survey Analysis and Area Based EditoR (SABER) software. Variability in depth and position of seafloor features observed multiple times from repeat passes of the AUV, and junctioning of the AUV acquired bathymetry with bathymetry acquired from a surface platform are used to evaluate the TPU model and to characterize the AUV acquired data

Development and testing of a dual accelerometer vector sensor for AUV acoustic surveys

This paper presents the design, manufacturing and testing of a Dual Accelerometer Vector Sensor (DAVS). The device was built within the activities of theWiMUST project, supported under the Horizon 2020 Framework Programme, which aims to improve the efficiency of the methodologies used to perform geophysical acoustic surveys at sea by the use of Autonomous Underwater Vehicles (AUVs). The DAVS has the potential to contribute to this aim in various ways, for example, owing to its spatial filtering capability, it may reduce the amount of post processing by discriminating the bottom from the surface reflections. Additionally, its compact size allows easier integration with AUVs and hence facilitates the vehicle manoeuvrability compared to the classical towed arrays. The present paper is focused on results related to acoustic wave azimuth estimation as an example of its spatial filtering capabilities. The DAVS device consists of two tri-axial accelerometers and one hydrophone moulded in one unit. Sensitivity and directionality of these three sensors were measured in a tank, whilst the direction estimation capabilities of the accelerometers paired with the hydrophone, forming a vector sensor, were evaluated on a Medusa Class AUV, which was sailing around a deployed sound source. Results of these measurements are presented in this paper.European Union [645141]info:eu-repo/semantics/publishedVersio