Tethys-class long range AUVs - extending the endurance of propeller-driven cruising AUVs from days to weeks

Hobson, Brett; Bellingham, James; Kieft, Brian; McEwen, R.; Godin, M. A.; Zhang, Yanwu

doi:10.1109/auv.2012.6380735

Cited by 107 publications

(79 citation statements)

References 12 publications

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“…Longer term benefits include new capabilities for extended duration missions and improved navigation in polar regions. Long-range AUVs (Hobson et al (2012);Furlong et al (2012)) are enabling the long-duration biogeochemical process studies necessary for understanding the effects of climate change on the ocean; however, many of these missions will occur in the mid-water column where our navigation capabilities are presently the weakest. The results herein provide a solution in the vertical degree of freedom and serve as a foundation for future work in horizontal ADCP-aided navigation (Medagoda et al (2015)).…”

Section: Resultsmentioning

confidence: 99%

“…Within the oceanographic community, AUVs are used for tasks such as seafloor mapping (e.g., Caress et al (2012); Kelley et al (2005)), habitat monitoring (e.g., Williams et al (2012)), optical surveys (e.g., Singh et al (2004a)), high-resolution magnetic surveys (Tivey et al (1998)), climate change research (Schofield et al (2010)) and localizing hydrothermal and hydrocarbon plumes (German et al (2008) and Camilli et al (2010), respectively). An advantage of AUVs over other ocean observation methods is the potential for reduced costs (i.e., decreased dependence on manned surface vessels) as well as increased mission duration -especially as long-range AUVs mature (Hobson et al (2012);Furlong et al (2012)) and longer duration missions (on the order of weeks or months) increasingly become a reality.…”

Section: Introductionmentioning

confidence: 99%

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Mid-water current aided localization for autonomous underwater vehicles

et al. 2016

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Survey-class Autonomous Underwater Vehicles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the midwater column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Current Profiler (ADCP) mode of the DVL during descent, along with data from other sensors, the method discussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Mapping (SLAM) and on descents up to 3km deep with the Sentry AUV.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mid-water current aided localization for autonomous underwater vehicles

et al. 2016

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“…In order to do so, they are designed with novel improvements to previous AUV classes, such as active buoyancy control, allowing them to sample processes at a desired depth without expending energy on propulsion. Two such examples of these AUVs are the Tethys [1] AUV built at MBARI, and which has active buoyancy control and utilizes custom energy saving strategies (such as power-down of motor controllers and non-essential systems), and the Folaga [2] AUV built by Graaltech, which similarly has active buoyancy control as well as an actuation mechanism that allows it to propel as a glider. We envision the use of this class of AUVs for long endurance multi AUV oceanographic sampling, and their emergence can be interpreted as an endorsement for the benefit of using swarms of AUVs for this purpose.…”

Section: Autonomous Underwater Vehiclesmentioning

confidence: 99%

Distributed autonomy and formation control of a drifting swarm of autonomous underwater vehicles

Rypkema¹

2015

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Recent advances in autonomous underwater vehicle (AUV) technology have led to their widespread acceptance and adoption for use in scientific, commercial, and defence applications in the underwater domain. At the same time, research progress in swarm robotics has seen swarm intelligence algorithms in use with greater effect on real-world robots in the field. A group of AUVs utilizing swarm intelligence concepts has the potential to address issues more effectively than a single AUV, and such a group can potentially open up new areas of application. Examples include the monitoring and tracking of highly dynamic oceanographic phenomena such as phytoplankton blooms and the use of an AUV swarm as a virtual acoustic receiver for sea-bottom seismic surveying or the monitoring of naturally occurring acoustic radiation from cracking ice. However, the limitations of the undersea environment places unique constraints on the use of existing swarm robotics approaches with AUVs. In particular, algorithms must be distributed and robust in the face of localization error and degraded communications.This work presents an investigation into one particular swarm strategy for a group of AUVs, termed formation control, with consideration to the constraints of the underwater domain. Four formation control algorithms, each developed and tested within the MOOS-IvP framework, are presented. In addition, a 'formation quality' metric is introduced. This metric is used in conjunction with a measure of formation energy expenditure to compare the efficacy of each behaviour during construction of a desired formation, and formation maintenance while it drifts in ocean currents. This metric is also used to compare robustness of each algorithm in the presence of vehicle failure and changing communication rate.

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“…6 These methods are generally based on linear approximations of the system's dynamics, and they require models to be built for both nominal and faulty states; diagnosis proceeds by comparing model output with observed behavior and using various techniques to explain any discrepancies. A survey of fault-detection strategies using onboard unmanned underwater vehicles has been presented by Antonelli.…”

Section: Model-basedmentioning

confidence: 99%

Detection of unanticipated faults for autonomous underwater vehicles using online topic models

Raanan

Bellingham

Zhang

et al. 2017

Journal of Field Robotics

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For robots to succeed in complex missions, they must be reliable in the face of subsystem failures and environmental challenges. In this paper, we focus on autonomous underwater vehicle (AUV) autonomy as it pertains to self-perception and health monitoring, and we argue that automatic classification of state-sensor data represents an important enabling capability. We apply an online Bayesian nonparametric topic modeling technique to AUV sensor data in order to automatically characterize its performance patterns, then demonstrate how in combination with operatorsupplied semantic labels these patterns can be used for fault detection and diagnosis by means of a nearest-neighbor classifier. The method is evaluated using data collected by the Monterey Bay Aquarium Research Institute's Tethys long-range AUV in three separate field deployments. Our results show that the proposed method is able to accurately identify and characterize patterns that correspond to various states of the AUV, and classify faults at a high rate of correct detection with a very low false detection rate.

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Tethys-class long range AUVs - extending the endurance of propeller-driven cruising AUVs from days to weeks

Cited by 107 publications

References 12 publications

Mid-water current aided localization for autonomous underwater vehicles

Mid-water current aided localization for autonomous underwater vehicles

Distributed autonomy and formation control of a drifting swarm of autonomous underwater vehicles

Detection of unanticipated faults for autonomous underwater vehicles using online topic models

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