In this paper, we propose a novel method for autonomously seeking out sparsely distributed targets in an unknown underwater environment. Our Sparse Adaptive Search and Sample (SASS) algorithm mixes low-altitude observations of discrete targets with high-altitude observations of the surrounding substrates. By using prior information about the distribution of targets across substrate types in combination with belief modelling over these substrates in the environment, high-altitude observations provide information that allows SASS to quickly guide the robot to areas with high target densities. A maximally informative path is autonomously constructed online using Monte Carlo Tree Search with a novel acquisition function to guide the search to maximise observations of unique targets. We demonstrate our approach in a set of simulated trials using a novel generative species model. SASS consistently outperforms the canonical boustrophedon planner by up to 36% in seeking out unique targets in the first 75-90% of time it takes for a boustrophedon survey. Additionally, we verify the performance of SASS on two real world coral reef datasets.

We report a comparative performance evaluation, using at-sea field data, of recently reported methods for the problem of in-situ calibration of the alignment rotation matrix between Doppler sonar velocity sensors and inertial navigation sensors arising in the navigation of underwater vehicles. Most previously reported solutions to this alignment calibration problem require the use of absolute navigation fixes of the underwater vehicle, thus requiring additional navigation sensors and/or beacons to be located externally and apart from the underwater vehicle. We briefly review four recently reported alignment calibration methods employing only internal vehicle navigation sensors for velocity, acceleration, attitude, and depth. We report the results of comparative analysis of the performance of these recently reported methods and a previously reported method with navigation data from deep-water survey missions of the Sentry autonomous underwater vehicle conducted in March, 2011 in the Kermadec Arc in the Southern Pacific Ocean. The results reveal consistent differences in performance of the various methods when analyzed on navigation data from several different vehicle dives.

Autonomous underwater vehicles (AUVs) are commonly used to support oceanographic science by providing water-column mapping, seafloor bathymetric and photographic survey, and deep-sea exploration capabilities. In practice, the mapping activities carried out by AUVs consist of flying either pre-programmed tracklines (most propeller-driven AUVs), or else reporting data to human operators at regular intervals that permit retasking (typical for month-long underwater glider deployments). AUVs equipped with the ability to reason about scientific objectives in real time could significantly increase the value of individual deployments by enabling sampling efforts to be focused on targets or areas identified autonomously or semi-autonomously as scientifically interesting [1]. In this paper, we focus on AUV autonomy as it pertains to water-column sensing and argue that the classification of water-column sensor data represents an important enabling capability. We demonstrate practical, semi-supervised classification of water-column sensor data using a particular Bayesian, non-parametric clustering method, the Variational Dirichlet Process, combined with operator-supplied semantic labeling. The method is applied to the detection of a deep subsea hydrocarbon plume using data collected by the Woods Hole Oceanographic's Sentry AUV during an expedition to the Gulf of Mexico following the Deepwater Horizon blowout disaster.

This paper reports the Sentry autonomous underwater vehicle and its deployment on two cruises in response to the Deepwater Horizon oil spill. The first cruise, in June 2010, coupled Sentry with the TETHYS mass spectrometer to track and localize a subsea hydrocarbon plume at a depth of approximately 1100m going at least 30km from the oil spill site. In December 2010, Sentry mapped and photographed deep-sea biological communities for follow-up observations and sampling with the Alvin manned submersible. These cruises demonstrate how robots and novel sensing technologies contributed to the oil spill assessment and the broader impact of technologies developed for basic research.

This paper reports an overview of the navigation and control system design for the new Nereus hybrid underwater robotic vehicle (HROV). Vehicle performance during its first sea trials in November 2007 near Hawaii, and in May and June 2009 in the Challenger Deep of the Mariana Trench is reported. During the latter expedition, the vehicle successfully performed scientific observation and sampling operations at depths exceeding 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 tethered 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), an electro-hydraulic manipulator arm, and sampling instruments. The Nereus vehicle is designed to render all parts of the Earth's seafloor accessible to oceanographic science.

In this paper we address the problem of localizing active hydrothermal vents on the seafloor using an autonomous underwater vehicle (AUV). Woods Hole Oceanographic Institution's Autonomous Benthic Explorer (ABE) (see Fig.1) AUV has been successfully used in several hydrothermal vents prospecting missions. Recently, a three-stage nested approach [1] has been introduced. It consists in surveys at different altitudes to finally photograph the venting structures flying very close to the seafloor. In this paper, we introduce a new strategy for ABE's movement in surveys at higher altitude (phase-1): ABE moves along predesigned tracklines sampling the seawater; when some conditions on collected data are encountered, it starts a spiral movement to fly over areas likely to contain active vent fields. Results of the proposed algorithm tested on data coming from previous ABE's missions are shown and assess the efficiency and reliability of the method.

This paper addresses the problem of autonomous localization of multiple gas or odor sources in an indoor environment with no strong airflow. In our approach, a robot iteratively builds an occupancy grid map from successive measurements of odor concentration. The resulting map shows the probability of each discrete cell in the map containing an active plume source. Our method is based on a recent adaptation of Bayesian occupancy grid mapping (OGM) to the chemical plume source localization problem. We present experimental results that demonstrate the utility of the approach.

This paper demonstrates a method for using an autonomous underwater vehicle to measure the velocity of a deep-sea hydrothermal plume. Based on a system identification experiment conducted in the actual operating environment, we extracted a dynamic model of the vehicle's vertical response, then used that model to infer the vertical water velocity of rising plumes in the main observation area. The results are compared to a more direct measurement using an acoustic current meter.

In this paper we present a methodology for high resolution acoustic bathymetric mapping from a robotic underwater vehicle. Based on data obtained from navigation, attitude, and bathymetric sensors we show that precise calibration of attitude sensors is critical to obtaining high precision bathymetric surveys. We present an in-situ method for precision attitude sensor calibration based upon specific vehicle maneuvers. This method is demonstrated using data from an acoustic bathymetric survey of an archaeological site in the Mediterranean conducted by the authors with the Jason remotely operated vehicle.

The rugged, mountainous regions of the deep seafloor hold both great scientific interest as well as a host of difficult challenges for autonomous robots. Exploiting its abilities for precise navigation, trackline following, and bottom-following in rough terrain, the autonomous benthic explorer collected fine-scale bathymetry, magnetic, temperature, optical backscatter and conductivity over a rugged, neo-volcanic and active tectonic zone of the southern east Pacific rise (18/spl deg/S). We combined these data sets to produce a variety of maps showing dramatic terrain in unprecedented detail. We also created the first systematic map view of hydrothermal temperature anomalies over the mid-ocean ridge.

New low-cost commercially available bottom-lock Doppler sonars can augment or replace the acoustic time-of-flight navigation systems commonly employed for three-dimensional underwater robot vehicle navigation. The paper first reviews conventional techniques for underwater vehicle navigation, and describes a Doppler-based navigation system developed by the authors. Second, we identify principal limitations to the bottom-track precision of Doppler based navigation systems. Third, we analyze the effect of heading-sensor errors on Doppler bottom-track precision. Experimental results compare bottom-track error resulting from a Doppler navigation using low-precision magnetic heading sensor with bottom-track error resulting from a high-precision a ring-laser gyroscope. The experiments were conducted during a field deployment in which the new robot navigation system enabled precision acoustic and optical survey as well as minimally invasive object recovery from hydrothermal vents in the Guaymas Basin, Gulf of California at 27/spl deg/N 111.5/spl deg/W, at 2000 m depth.

We look at some of the important requirements for making autonomous underwater vehicles (AUVs) more ubiquitous in their role in oceanographic research in the deep ocean. We show how some of the constraints in working underwater lead to unique solutions for complex multidimensional sets of design possibilities for working with real vehicles in the deep ocean. We identify three components in elaborating on our system optimization concepts-the use of power, sensing strategies, and navigation. We analyse several possible deployments from a theoretical and practical standpoint in which the effects of each of these components interact. We look at the tasks associated with long distance transits, with conducting a sonar survey as well as the task associated with navigating multiple vehicles in the same acoustic network.

This paper presents experiments with a new algorithm for the unsteady (transient) thrust control bladed-propeller marine thrusters. Comparative experiments over a wide range of unsteady operating conditions with three different thrust control algorithms demonstrate the model-based control algorithm to offer transient thrust-control performance superior to that of its nonmodel-based counterpart. The use of improved unsteady thrust control on underwater vehicles promises to reduce the well-documented limit-cycling of dynamically-positioned marine vehicle position during low-speed manuevering.

The design, implementation, and deployment of a new computer system for closed loop control of the JASON underwater robot at full ocean depth is described. Several design principles for the rapid development of large scale robot control systems are articulated and illustrated with examples from the JASON system. The use of subsystems with differing computer architectures permit effective matching of capability to function. A two-part system design partitions safety-critical from nonsafety-critical subsystems for cost-effective implementation and enhancement. A machine-independent message-passing communication protocol, JasonTalk, enables integration of disparate computer architectures into a unified system. Results from a recent JASON deployment in the Pacific Ocean are discussed.

It is shown that adaptive extensions of sliding control are effective for precise control of underwater vehicles through experiments with an actual vehicle. Adaptive sliding control permits direct nonlinear control system design, including online parameter estimation. Experimental results are detailed which were obtained from a tethered underwater vehicle equipped with a precision broadband acoustic navigation system and three-degree-of-freedom attitude instrumentation controlled through a real-time network of transputers. Simulation and experimental trials show online determination of vehicle parameters such as mass and nonlinear drag.>

Presents a review of some of the areas to which supervisory control theory has been applied and indicate some of the ideas which have as yet not been followed up but which were introduced by Sheridan and his students. Topics covered include: modeling manual and supervisory control tasks; supervisory control of deep-ocean vehicles; technology problems and technology solutions; human integration in a complex world; supervisory control in distributed decision-making; and human supervisor control models in advanced manufacturing systems.>

An overall concept and specific system elements for teleoperated vehicles and manipulators are presented. The approach emphasizes continuous, real-time sharing of control between both the human operator and the computer system and is intended for application to the JASON underwater vehicle now in development. As JASON will have extremely high communications bandwidth available through a fiber optic cable, the emphasis will be on aiding and extending the capabilities of the human operator. Specific elements presented include task-resolved motion specification, rule-based inverse kinematics, and robust and adaptive nonlinear tracking control.