-
[show abstract]
[hide abstract]
ABSTRACT: Survey class Autonomous Underwater Vehicles (AUVs) rely on Doppler Velocity Logs (DVL) for precise navigation near the seafloor. In cases where the seafloor depth is greater than the DVL bottom lock range, transiting from the surface where GPS is available to the seafloor presents a localisation problem since both GPS and DVL are unavailable in the mid-water column. This is traditionally addressed by using acoustic positioning systems, which take extra time to deploy or require a tracking vessel. Such systems increase the costs of operating in deep waters and reduce the flexibility of AUV operations. This paper proposes an alternative approach to navigation in the mid-water column that exploits the stability of current profiles of water columns over short periods of time. Observation of these currents are possible with the ADCP (Acoustic Doppler Current Profiler) mode of the DVL. Results with real data from missions with the Sirius AUV show how the full integration of water column descent with the ADCP, seafloor view-based SLAM (Simultaneous Localisation And Mapping), and ascent to the sea surface with ADCP gives results similar to having continuous bottom lock and shows potential to act as an alternative to acoustic localisation.
Robotics and Automation (ICRA), 2011 IEEE International Conference on; 06/2011
-
OCEANS 2011; 01/2011
-
[show abstract]
[hide abstract]
ABSTRACT: Robotic agents that can explore and sample in a completely unsupervised fashion could greatly increase the amount of scientific data gathered in dangerous and inaccessible environments. Our application is imaging the benthos using an autonomous underwater vehicle with limited communication to surface craft. Robotic exploration of this nature demands in situ data analysis. To this end, this paper presents results of using a Gaussian Mixture Model (GMM), a Hidden Markov Model (HMM) filter, an Infinite Gaussian Mixture Model (IGMM) and a Variation Dirichlet Process model (VDP) for the classification of benthic habitats. All of the models are trained using unsupervised methods. Furthermore, the IGMM and VDP are trained without knowing the the number of classes in the dataset. It was found that the sequential information the HMM filter provides to the classification process adds lag to the habitat boundary estimates, reducing the classification accuracy. The VDP proved to be the most accurate classifier of the four tested, and also one of the fastest to train. We conclude that the VDP is a powerful model for entirely autonomous labelling of benthic datasets.
Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on; 11/2010
-
[show abstract]
[hide abstract]
ABSTRACT: Currently, the majority of AUV missions follow fixed pre-programmed surveys. In exploration missions, the environment is unknown and pre-programmed surveys risk wasting limited resources on data with little scientific value. This risk can be mitigated by allowing autonomous agents to adapt their behaviour to suit the environment and the scientific goals of the survey. This paper presents a method for performing adaptive surveys which combines elements from the fields of perception, machine learning and planning. During exploration, a Gaussian mixture model is used to classify sensor data. The classes returned by the Gaussian mixture model are modelled spatially using a Gaussian process classifier. This spatial model is used to guide the agent's exploration into informative areas of the environment using value iteration. The advantage of using adaptive surveys and its potential for outperforming pre-programmed surveys is demonstrated in an example application.
OCEANS 2010; 10/2010
-
[show abstract]
[hide abstract]
ABSTRACT: Survey class Autonomous Underwater Vehicles (AUVs) rely on Doppler Velocity Logs (DVL) for precise navigation near the seafloor. In cases where the seafloor depth is greater than the DVL bottom lock range, transiting from the surface, where GPS is available, to the seafloor presents a localisation problem since both GPS and DVL are unavailable in the midwater column. This paper proposes an alternative approach to navigation in the mid-water column that exploits the fact that current profiles of water columns are stable over time. With reobservation of these currents with the ADCP (Acoustic Doppler Current Profiler) mode of the DVL during descent, along with sensor fusion of other low cost sensors, position error growth can be constrained to near the initial velocity uncertainty of the vehicle at the sea surface during the dive, and following DVL bottom lock, the entire velocity history is constrained to an error similar to the DVL velocity uncertainty. Simulation results show performance with sensor fusion of a low cost IMU and DVL bottom lock at the sea floor can achieve 20 cm per minute (2 σ) position error growth. Results with real data from an Autonomous Benthic Explorer (ABE) dive show that this method is applicable and a promising approach to navigation for untended deepwater autonomous vehicle operations.
OCEANS 2010 IEEE - Sydney; 06/2010
-
[show abstract]
[hide abstract]
ABSTRACT: There is a need for truly unsupervised approaches to understanding acquired data in autonomous exploratory missions with minimal, or zero, bandwidth communication. This paper presents results of using a Bayesian non-parametric Dirichlet Process mixture model - the Infinite Gaussian Mixture Model (IGMM) - for the classification of benthic habitats. The IGMM is trained completely autonomously, without being given labelled data, or knowing the number of habitats present. It is able to infer the number of habitats present in the training data, and is also able to infer the presence of habitats in the test data that were not present in the training data. This is a powerful model for entirely autonomous labelling of benthic datasets, and will be used as the basis of completely autonomous approaches to understanding data in the future.
OCEANS 2010 IEEE - Sydney; 06/2010
-
[show abstract]
[hide abstract]
ABSTRACT: Benthic imaging AUVs can deliver down-looking imagery with consistent altitude and illumination. These images are well suited to image matching routines that form the basis for mosaicking, vision-based Simultaneous Localisation and Mapping (SLAM) and 3D visual reconstructions. We show how these same visual constraints can be used to (1) improve the real time dead-reckoning accuracy of AUVs equipped with magnetic compasses and (2) to quantify the accuracy of various corrections applied to measured pressure to determine vehicle depth. We present results from full coverage optical imaging surveys conducted in the field by our AUV to illustrate the impact of these refinements on the ability of the vehicle to successfully complete its missions and the quality of the resulting seafloor models.
OCEANS 2010 IEEE - Sydney; 06/2010
-
[show abstract]
[hide abstract]
ABSTRACT: This paper describes an approach to achieving high resolution, repeated benthic surveying using an Autonomous Underwater Vehicle (AUV). A stereo based Simultaneous Localisation and Mapping (SLAM) technique is used to estimate the trajectory of the vehicle during multiple overlapping grid based surveys. The vehicle begins each dive on the surface and uses GPS to navigate to a designated start location. Once it reaches the designated location on the surface, the vehicle dives and executes a pre-programmed grid survey, collecting co-registered high resolution stereo images, multibeam sonar and water chemistry data. A suite of navigation instruments are used while the vehicle is underway to estimate its pose relative to the local navigation frame. Following recovery of the vehicle, the SLAM technique is used to refine the estimated vehicle trajectory and to find loop closures both within each survey and between successive missions to co-register the dives. Results are presented from recent deployments of the AUV Sirius at a site in North Eastern Tasmania. The objective of the deployments described in this work were to document the behaviour of barrens-forming sea sea urchins which have recently become resident in the area. The sea urchins can overgraze luxuriant kelp beds that once dominated these areas, leaving only rocky barrens habitat. The high resolution stereo images and resulting three dimensional surface models allow the nocturnal behaviour of the animals, which emerge to feed predominantly at night, to be described. Co-registered images and resulting habitat models collected during the day and at night are being analysed to describe the behaviour of the sea urchins in more detail.
Robotics and Automation (ICRA), 2010 IEEE International Conference on; 06/2010
-
A.D. Bowen,
D.R. Yoerger,
C. Taylor,
R. McCabe,
J. Howland,
D. Gomez-Ibanez,
J.C. Kinsey,
M. Heintz,
G. McDonald,
D.B. Peters, [......],
T. Shank,
L.L. Whitcomb,
S.C. Martin,
S.E. Webster, M.V. Jakuba,
B. Fletcher,
C. Young,
J. Buescher,
P. Fryer,
S. Hulme
[show abstract]
[hide abstract]
ABSTRACT: This paper reports the results of sea trials of the Nereus hybrid underwater robotic vehicle (HROV) conducted in May and June 2009 in the Challenger Deep of the Mariana Trench, where the vehicle successfully performed scientific observation and sampling operations at hadal depths of 10,903 m. The Nereus underwater vehicle is designed to perform scientific survey and sampling to the full depth of the ocean - significantly deeper than the depth capability of all other present-day operational vehicles. For comparison, the second deepest underwater vehicle currently operational worldwide can dive to 7,000 m maximum depth. Nereus operates in two different modes. For broad-area survey, the vehicle can operate untethered as an autonomous underwater vehicle (AUV) capable of exploring and mapping the sea floor with sonars and cameras. Nereus can be converted at sea to become a remotely operated vehicle (ROV) to enable close-up imaging and sampling. The ROV configuration incorporates a lightweight fiber-optic tether for high-bandwidth, real-time video and data telemetry to the surface enabling high-quality teleoperation. A manipulator, lightweight hydraulic power unit, and sampling instruments are added to provide sampling capabilities. This paper reports a brief overview of the Nereus vehicle design, and reviews the initial results of the eight dives conducted on this expedition, including two dives to more than 10,900 m depth. The Nereus vehicle is designed to render all parts of the Earth's seafloor reachable and the sea trials of its full-ocean depth capability in May and June 2009 were successful.
OCEANS 2009, MTS/IEEE Biloxi - Marine Technology for Our Future: Global and Local Challenges; 11/2009
-
A.D. Bowen,
D.R. Yoerger,
C. Taylor,
R. McCabe,
J. Howland,
D. Gomez-Ibanez,
J.C. Kinsey,
M. Heintz,
G. McDonald,
D. Peters,
C. Young,
J. Buescher,
B. Fletcher,
L.L. Whitcomb,
S.C. Martin,
S.E. Webster, M.V. Jakuba
[show abstract]
[hide abstract]
ABSTRACT: The Nereus vehicle will enable scientists to explore remote regions of the oceans, such as under the polar ice caps and deep trenches, up to depths of 10 972m (36 000ft). Technology limitations have prevented routine, cost-effective access to these remote regions, and the final 4500m of the ocean remain largely unexplored. New solutions to deep diving are described. The Nereus hybrid remotely operated vehicle (HROV) is designed for exploration and research needs as a single system. It can operate as an autonomous vehicle for seafloor surveys, or in a tethered/ROV mode to sample rocks or deep-sea animals The HROV Nereus transforms between its two modes of operation to accomplish all these tasks during a single cruise deployment. Sea trials of Nereus took place off the Hawaiian Islands at 2500m in November 2007. An overview of the vehicle and results from its initial trials are reported here.
Underwater Technology The International Journal of the Society for Underwater 06/2009; 28(3):79-89.
-
A.D. Bowen,
D.R. Yoerger,
C. Taylor,
R. McCabe,
J. Howland,
D. Gomez-Ibanez,
J.C. Kinsey,
M. Heintz,
G. McDonald,
D.B. Peters,
B. Fletcher,
C. Young,
J. Buescher,
L.L. Whitcomb,
S.C. Martin,
S.E. Webster, M.V. Jakuba
[show abstract]
[hide abstract]
ABSTRACT: This paper reports an overview of the new Nereus hybrid underwater vehicle and summarizes the vehicle's performance during its first sea trials in November 2007. Nereus is a novel operational underwater vehicle designed to perform scientific survey and sampling to the full depth of the ocean of 11,000 meters - almost twice the depth of any present-day operational vehicle. Nereus operates in two different modes. For broad area survey, the vehicle can operate untethered as an autonomous underwater vehicle (AUV) capable of exploring and mapping the sea floor with sonars and cameras. For close up imaging and sampling, Nereus can be converted at sea to operate as a tethered remotely operated vehicle (ROV). This paper reports the overall vehicle design and design elements including ceramic pressure housings and flotation spheres; manipulator and sampling system; light fiber optic tether; lighting and imaging; power and propulsion; navigation; vehicle dynamics and control; and acoustic communications.
OCEANS 2008; 10/2008
