Augmented Terrain-Based Navigation to Enable Persistent Autonomy for Underwater Vehicles

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

doi:10.1109/irc.2017.36

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…We found that the ATBN framework was reliable for localisation tasks, and could provide pose estimation at accuracies similar to GPS. Further research has been conducted, and for a more detailed analysis of the field trials and corresponding results, we encourage the interested reader to consider [28,29,42].…”

Section: Resultsmentioning

confidence: 99%

“…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%

“…To further improve TBN, a method of creating an augmented terrain map that combines both bathymetry information and physical water data collected was proposed and tested, with results presented in [28]. The assumption that including physical water data into the terrain map provides a reliable model comes from the concept of Environmental or ecological niche models.…”

Section: Extending the Application Of Environmental Niche Modellingmentioning

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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Section: Resultsmentioning

confidence: 99%

Section: Construction Of An Environmental Niche Modelmentioning

confidence: 99%

Section: Extending the Application Of Environmental Niche Modellingmentioning

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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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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Energy-efficient control strategies for updating an augmented terrain-based navigation map for autonomous underwater navigation

Mileyko

Reis

Chyba

et al. 2017

2017 IEEE Conference on Control Technology and Applications (CCTA)

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Augmented Terrain-Based Navigation to Enable Persistent Autonomy for Underwater Vehicles

Cited by 10 publications

References 18 publications

Development of environmental niche models for use in underwater vehicle navigation

Development of environmental niche models for use in underwater vehicle navigation

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

Energy-efficient control strategies for updating an augmented terrain-based navigation map for autonomous underwater navigation

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