Position USBL/DVL sensor-based navigation filter in the presence of unknown ocean currents

Morgado, Manuel; Batista, Pedro; Oliveira, Paulo; Silvestre, Carlos

doi:10.1109/cdc.2010.5717740

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…It is difficult to realize high-precision navigation and positioning by relying on the navigation and positioning information obtained from a single sensor. In order to meet the navigation requirements, SINS and DVL are usually used in combination [8]. The SINS/DVL integrated navigation system is used for the underwater navigation and positioning of the AUV.…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis of Deep-Sea AUV Based on Multi-Sensor Integrated Navigation and Positioning

Liu,

Sun,

et al. 2024

Remote Sensing

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The operation of underwater vehicles in deep waters is a very challenging task. The use of AUVs (Autonomous Underwater Vehicles) is the preferred option for underwater exploration activities. They can be autonomously navigated and controlled in real time underwater, which is only possible with precise spatio-temporal information. Navigation and positioning systems based on LBL (Long-Baseline) or USBL (Ultra-Short-Baseline) systems have their own characteristics, so the choice of system is based on the specific application scenario. However, comparative experiments on AUV navigation and positioning under both systems are rarely conducted, especially in the deep sea. This study describes navigation and positioning experiments on AUVs in deep-sea scenarios and compares the accuracy of the USBL and LBL/SINS (Strap-Down Inertial Navigation System)/DVL (Doppler Velocity Log) modes. In practice, the accuracy of the USBL positioning mode is higher when the AUV is within a 60° observation range below the ship; when the AUV is far away from the ship, the positioning accuracy decreases with increasing range and observation angle, i.e., the positioning error reaches 80 m at 4000 m depth. The navigational accuracy inside and outside the datum array is high when using the LBL/SINS/DVL mode; if the AUV is far from the datum array when climbing to the surface, the LBL cannot provide accurate position calibration while the DVL fails, resulting in large deviations in the SINS results. In summary, the use of multi-sensor combination navigation schemes is beneficial, and accurate position information acquisition should be based on the demand and cost, while other factors should also be comprehensively considered; this paper proposes the use of the LBL/SINS/DVL system scheme.

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Section: Introductionmentioning

confidence: 99%

Experimental Analysis of Deep-Sea AUV Based on Multi-Sensor Integrated Navigation and Positioning

Liu,

Sun,

et al. 2024

Remote Sensing

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“…The specific case of aided navigation based on single range measurements has been addressed by the authors in . A USBL sensor‐based solution to the problem of underwater navigation, which resorts to similar tools as this paper, is proposed in .…”

Section: Introductionmentioning

confidence: 99%

Sensor‐Based Long Baseline Navigation: Observability Analysis and Filter Design

Batista¹,

Silvestre²,

Oliveira³

2013

Asian Journal of Control

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This paper presents a novel long baseline (LBL) position and velocity navigation filter for underwater vehicles based directly on the sensor measurements. The solution departs from previous approaches as the range measurements are explicitly embedded in the filter design, therefore avoiding inversion algorithms and allowing also the consideration of the cases of reduced numbers of readings, in particular when there are only two or three distance measurements. The nonlinear system dynamics are considered to their full extent and no linearizations are carried out whatsoever. The filter error dynamics are globally exponentially stable (GES) and it is shown, in a realistic simulation environment, that the filter achieves similar performance to the extended Kalman filter (EKF) and outperforms linear position and velocity filters based on algebraic estimates of the position obtained from the range measurements.

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“…Furthermore, relative velocity control removes the unknown term νc from the velocity feedback loop. The relative velocity νr is measurable via, for example, a Doppler velocity log (DVL) [58].…”

Section: B the Control Objectivementioning

confidence: 99%

Integral Line-of-Sight Guidance and Control of Underactuated Marine Vehicles: Theory, Simulations, and Experiments

Caharija

Pettersen

Bibuli

et al. 2016

IEEE Trans. Contr. Syst. Technol.

275

158

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Abstract-This paper presents an extensive analysis of the integral line-of-sight (ILOS) guidance method for path following tasks of underactuated marine vehicles, operating on and below the sea surface. It is shown that due to the embedded integral action, the guidance law makes the vessels follow straight lines by compensating for the drift effect of environmental disturbances such as currents, wind and waves. The ILOS guidance is first applied to a 2D model of surface vessels that includes the underactauted sway dynamics of the vehicle as well as disturbances in the form of constant irrotational ocean currents and constant dynamic, attitude dependent, forces. The actuated dynamics are not taken into account at this point. A Lyapunov closed loop analysis yields explicit bounds on the guidance law gains to guarantee uniform global asymptotic stability (UGAS) and uniform local exponential stability (ULES).The complete kinematic and dynamic closed loop system of the 3D ILOS guidance law is analyzed next, hence extending the analysis to underactuated AUVs for 3D straight-line path following applications in the presence of constant irrotational ocean currents. The actuated surge, pitch and yaw dynamics are included in the analysis where the closed loop system forms a cascade, and the properties of UGAS and ULES are shown. The 3D ILOS control system is a generalization of the 2D ILOS guidance. Finally, results from simulations and experiments are presented to validate and illustrate the theoretical results, where the 2D ILOS guidance is applied to the CART and the LAUV vehicles.

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Position USBL/DVL sensor-based navigation filter in the presence of unknown ocean currents

Cited by 9 publications

References 29 publications

Experimental Analysis of Deep-Sea AUV Based on Multi-Sensor Integrated Navigation and Positioning

Experimental Analysis of Deep-Sea AUV Based on Multi-Sensor Integrated Navigation and Positioning

Sensor‐Based Long Baseline Navigation: Observability Analysis and Filter Design

Integral Line-of-Sight Guidance and Control of Underactuated Marine Vehicles: Theory, Simulations, and Experiments

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