A Toolbox of Aiding Techniques for the HUGIN AUV Integrated Inertial Navigation System

Jalving, B.; Gade, Kenneth; Hagen, Ove Kent; Vestgård, K.

doi:10.4173/mic.2004.3.3

Cited by 51 publications

(22 citation statements)

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“…They are used in DR [18,19], in combined LBL/DR navigation systems [20,21], or as an aiding input to an INS [10,22]. In some cases, upward-looking DVLs have been used to measure vehicle velocity relative to overhead ice [23].…”

Section: Doppler Velocity Logmentioning

confidence: 99%

“…It is more common that an a priori map is not available-either the mission is to explore a new area, or to measure and characterize changes in the terrain. In this case, the vehicle might generate incremental maps from sensor data on the fly (e.g., [10,36]) or use simultaneous localization and mapping (SLAM) techniques (e.g., [37][38][39]). …”

Section: Terrain-relative Navigationmentioning

confidence: 99%

“…When a surface ship is in the area, it can track an underwater vehicle using an ultrashort baseline (USBL) system (e.g., [10,12]). A ship-mounted transducer acoustically interrogates a transponder mounted on the vehicle; the multi-element transducer measures the travel time and the phase of the reply, enabling the system to calculate range, bearing, and azimuth to the transponder.…”

Section: Ultra-short Baseline Acoustic Trackingmentioning

confidence: 99%

“…By plotting multiple ranges from one beacon along the dead reckoned vehicle trajectory, virtual long baseline (VLBL) enables the computation of a 'running fix' of the vehicle position [9]. With tight integration to an inertial navigation system (INS), the underwater transponder positioning (UTP) principle [10,11] also leverages the changing pose of the vehicle relative to the transponder to estimate vehicle position. In each of these methods, the geographic position of the transponder must still be surveyed.…”

Section: Acoustic Positioning Systemsmentioning

confidence: 99%

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Contributions to automated realtime underwater navigation

Stanway¹

2012

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This dissertation presents three separate-but related-contributions to the art of underwater navigation. These methods may be used in postprocessing with a human in the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is on automated approaches that can be used in realtime. The three research threads are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column, and iii) model-driven delayed measurement fusion. Contributions to each of these areas have been demonstrated in simulation, with laboratory data, or in the field-some have been demonstrated in all three arenas.The solution to the in situ navigation sensor alignment problem is an asymptotically stable adaptive identifier formulated using rotors in Geometric Algebra. This identifier is applied to precisely estimate the unknown alignment between a gyrocompass and Doppler velocity log, with the goal of improving realtime dead reckoning navigation. Laboratory and field results show the identifier performs comparably to previously reported methods using rotation matrices, providing an alignment estimate that reduces the position residuals between dead reckoning and an external acoustic positioning system. The Geometric Algebra formulation also encourages a straightforward interpretation of the identifier as a proportional feedback regulator on the observable output error. Future applications of the identifier may include alignment between inertial, visual, and acoustic sensors.The ability to link the Global Positioning System at the surface to precision dead reckoning near the seafloor might enable new kinds of missions for autonomous underwater vehicles. This research introduces a method for dead reckoning through the water column using water current profile data collected by an onboard acoustic Doppler current profiler. Overlapping relative current profiles provide information to simultaneously estimate the vehicle velocity and local ocean current-the vehicle velocity is then integrated to estimate position. The method is applied to field data using online bin average, weighted least squares, and recursive least squares implementations. This demonstrates an autonomous navigation link between the surface and the seafloor without any dependence on a ship or external acoustic tracking systems. 3Finally, in many state estimation applications, delayed measurements present an interesting challenge. Underwater navigation is a particularly compelling case because of the relatively long delays inherent in all available position measurements. This research develops a flexible, model-driven approach to delayed measurement fusion in realtime Kalman filters. Using a priori estimates of delayed measurements as augmented states minimizes the computational cost of the delay treatment. Managing the augmented states with time-varying conditional process and measurement models ensures the approach works within the proven Kalman filter framework-without altering the filter structure or requiring any ad-hoc adjust...

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Section: Doppler Velocity Logmentioning

confidence: 99%

Section: Terrain-relative Navigationmentioning

confidence: 99%

Section: Ultra-short Baseline Acoustic Trackingmentioning

confidence: 99%

Section: Acoustic Positioning Systemsmentioning

confidence: 99%

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Contributions to automated realtime underwater navigation

Stanway¹

2012

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“…Furthermore, the DVL's ping rate is generally slow (3-7 Hz) and dependency of the DVL signal on the acoustic environment causes the DVL to occasionally be unavailable (Farrell, 2008). The other disadvantage of the DR approach arises from the inaccuracy of the velocity data caused by internal biasing errors, external random errors and also error in heading measurement which causes the position error to grow with time due to the error in transformation of the velocity from body frame to navigation frame and time integration of the velocity signal (Jalving et al, 2003).…”

mentioning

confidence: 99%

Improved Underwater Integrated Navigation System using Unscented Filtering Approach

Shabani

2015

J. Navigation

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In underwater navigation, the conventional Error State Kalman Filter (ESKF) is used for combining navigation data where due to first order linearization of the nonlinear equations of the dynamics and measurements, considerable error is induced in estimated error state and covariance matrices. This paper presents an underwater integrated inertial navigation system using the unscented filter as an improved nonlinear version of the Kalman filter family. The designed system consists of a strap-down inertial navigation system accompanying Doppler velocity log and depth meter. In the proposed approach, to use the nonlinear capabilities of the unscented filtering approach the integrated navigation system is implemented in a direct approach where the nonlinear total state dynamic and and measurement models are utilised without any linearization. To our knowledge, no results have been reported in the literature on the experimental evaluation of the unscented-based integrated navigation system for underwater vehicles. The performance of the designed system is studied using real measurements. The results of the lake test show that the proposed system estimates the vehicle's position more accurately compared with the conventional ESKF structure.2. Underwater Integrated Navigation System. 3. Error State Kalman Filter.4. Unscented Filtering.

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A 3D reactive collision avoidance algorithm for underactuated underwater vehicles

Wiig

Pettersen

Krogstad

2020

Journal of Field Robotics

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Avoiding collisions is an essential goal of the control system of autonomous vehicles. This paper presents a reactive algorithm for avoiding obstacles in a threedimensional space, and shows how the algorithm can be applied to an underactuated underwater vehicle. The algorithm is based on maintaining a constant avoidance angle to the obstacle, which ensures that a guaranteed minimum separation distance is achieved. The algorithm can thus be implemented without knowledge of the obstacle shape. The avoidance angle is designed to compensate for obstacle movement, and the flexibility of operating in 3D can be utilized to implement traffic rules or operational constraints. We exemplify this by incorporating safety constraints on the vehicle pitch and by making the vehicle seek to move behind the obstacle, while also minimizing the required control effort. The underactuation of the vehicle induces a sway and heave movement while turning. To avoid uncontrolled gliding into the obstacle, we account for this movement using a Flow frame controller, which controls the direction of the vehicleʼs velocity rather than just the pitch and yaw. We derive conditions under which it is ensured that the resulting maneuver is safe, and these results are verified trough simulations and through full-scale experiments on the Hugin HUS autonomous underwater vehicle. The latter demonstrates the performance of the proposed algorithm when applied to a case with unmodeled disturbances and sensor noise, and shows how the modular nature of the collision avoidance algorithm allows it to be applied on top of a commercial control system. 1 | INTRODUCTION Unmanned vehicles are often intended to operate with limited, delayed, or no human supervision. The environment they operate in can be dynamic or unknown, with only incomplete or partially erroneous a priori information available. This is often the scenario for autonomous underwater vehicles (AUVs), charged with exploration and mapping of the worldʼs oceans. The autonomous nature of AUV missions necessitates the ability to react to the environment, for example, to avoid collision with obstacles. The collision avoidance problem becomes particularly challenging if the vehicle is underactuated, as indeed many AUVs are. An underactuated vehicle is not able to control all degrees of freedom independently, and generally has second-order nonholonomic constraints, making it necessary to include the vehicle dynamics in the design and analysis of the control system (Pettersen & Egeland, 1996). In this paper, we present and analyze a collision avoidance algorithm called the constant avoidance angle algorithm, which we have implemented on a class of underactuated underwater vehicles.

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A Toolbox of Aiding Techniques for the HUGIN AUV Integrated Inertial Navigation System

Cited by 51 publications

References 1 publication

Contributions to automated realtime underwater navigation

Contributions to automated realtime underwater navigation

Improved Underwater Integrated Navigation System using Unscented Filtering Approach

A 3D reactive collision avoidance algorithm for underactuated underwater vehicles

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