Increasing persistent navigation capabilities for underwater vehicles with augmented terrain-based navigation

Reis, Gregory Murad; Fitzpatrick, Michael; Anderson, Jacob; Kelly, Jonathan; Bobadilla, Leonardo; Smith, Ryan N.

doi:10.1109/oceanse.2017.8084815

Cited by 5 publications

(9 citation statements)

References 22 publications

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“…Assumption 2: The measurement error of each measuring point h ij (X p ) obeys the same distribution N 0, (σ p ) 2 .…”

Section: Tap Confidence Intervalmentioning

confidence: 99%

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Improvements to Terrain Aided Navigation Accuracy in Deep-Sea Space by High Precision Particle Filter initialization

Wang

et al. 2020

IEEE Access

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AUV (autonomous underwater vehicles) are required to have long-term and high-precision positioning capability relative to seabed targets in most deep-sea exploration tasks. However, acoustic positioning error is positively correlated with its operating range and inertial navigation has inevitable accumulated time errors, neither of which provide precise AUV positions. TAN (terrain aided navigation) directly calculates the AUV position to the seabed terrain coordinate system by tracking the seabed topographic characteristics, which can guide the AUV to seabed target accurately. However, the initial TAN positioning error will increase with the AUV operation depth, which causes a large PF (particle filter) initialization error and particle coverage interval, and will affect the convergence and stability of TAN. To solve this problem, we first propose a TAP (terrain aided position) confidence interval model. We then use the confidence interval to constrain the initial particles to a smaller range. Finally, the validity of the algorithm is verified by playback simulation with ship-borne multi-beam sonar sensor measured data. The results show that the TAP confidence interval can reduce the coverage of the initial particle, and can improve the convergence speed and filtering accuracy of the TAN.

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“…Assumption 2: The measurement error of each measuring point h ij (X p ) obeys the same distribution N 0, (σ p ) 2 .…”

Section: Tap Confidence Intervalmentioning

confidence: 99%

“…The error in the DR system is v t , where v t is an additive white noise input. In TAN system, u t and v t are directly given by DR. E t is the error in the depth measurement at time t, the variance estimates are described in equation (2).…”

Section: Introductionmentioning

confidence: 99%

Improvements to Terrain Aided Navigation Accuracy in Deep-Sea Space by High Precision Particle Filter initialization

Wang

et al. 2020

IEEE Access

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“…Here, we choose a proxy fish species to demonstrate the niche determination methodology. In our previous work on augmented terrain-based navigation (ATBN), we relaxed this assumption and utilised all data parameters collected [28,29]. Although we have found utility with the developed ATBN approach, the environmental niche model presented here has an application in bounding a survey region, providing a theoretical foundation for our ATBN approach, and providing landmarks for use in relative localisation.…”

Section: Construction Of An Environmental Niche Modelmentioning

confidence: 99%

“…The aforementioned compromise between limiting time spent underwater in favour of positional accuracy, or the converse, illustrates the need for a novel methodology of localisation underwater. Previously, work by Reis and colleagues proposed augmenting traditional terrain-based navigation (TBN) in an underwater environment to improve localisation techniques devoid of a GPS signal [29,34,35]. TBN was initially developed for the localisation of long-range missiles and involves taking repeated altimeter measurements and comparing those to altimeter data in a stored array.…”

Section: Extending the Application Of Environmental Niche Modelling Tmentioning

confidence: 99%

“…Reis et al [29,35] incorporated an array of sensors to augment altimeter data in traditional TBN to shorten the convergence time for localisation and enable navigation over regions with low bathymetric relief. The principle of augmenting TBN with in situ measurements of physical water data is the novel contribution of this work and where the concept of an environmental niche model emerges into the application of navigation for underwater robots.…”

Section: Extending the Application Of Environmental Niche Modelling Tmentioning

confidence: 99%

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Development of environmental niche models for use in underwater vehicle navigation

Fitzpatrick

Reis

Anderson

et al. 2020

IET cyber-systems robotics

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This paper presents a review of Environmental Niche Modeling and a methodology for isolating the environmental niche of an aquatic species, given that prior information is available for characterising the physical tolerances for that species. To test and demonstrate our methodology, the environmental niche of the kelp bass has been isolated within Big Fisherman's Cove, Santa Catalina Island, CA, at specific confidence intervals. The motivation for this examination is to demonstrate the utility of ecological analysis in Robotics. Specifically, the utilisation of physical water properties to provide relative navigation and localisation for an aquatic robot. The environmental niches act as navigational landmarks in the seemingly featureless underwater environment. As water patches tend to stick together, this provides persistent landmarks for use in aquatic navigation problems. We provide the background and development of Environmental Niche Models, and present results from field trials for solving the navigation and localisation problem for underwater vehicles. Specifically, we provide results from a technique developed by the authors that utilises physical water parameters, e.g., temperature, salinity, chlorophyll, etc., to localise an underwater vehicle in a given region. The presented method leverages the concept of Environmental Niche Models to provide accurate position estimation that rivals GPS accuracy.

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Localization in Seemingly Sensory-Denied Environments through Spatio-Temporal Varying Fields

Fuentes

Bobadilla

Smith

2022

2022 Sixth IEEE International Conference on Robotic Computing (IRC)

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Increasing persistent navigation capabilities for underwater vehicles with augmented terrain-based navigation

Cited by 5 publications

References 22 publications

Improvements to Terrain Aided Navigation Accuracy in Deep-Sea Space by High Precision Particle Filter initialization

Improvements to Terrain Aided Navigation Accuracy in Deep-Sea Space by High Precision Particle Filter initialization

Development of environmental niche models for use in underwater vehicle navigation

Localization in Seemingly Sensory-Denied Environments through Spatio-Temporal Varying Fields

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