Autonomous Underwater Vehicle

With the advances in acoustic modem technology that enabled high-rate reliable communications, current research focuses on communication between various remote instruments within a network environment. Underwater acoustic (UWA) networks are generally formed by acoustically connected ocean-bottom sensors, autonomous underwater vehicles, and a surface station, which provides a link to an on-shore control center. While many applications require long-term monitoring of the deployment area, the battery-powered network nodes limit the lifetime of UWA networks. In addition, shallow-water acoustic channel characteristics, such as low available bandwidth, highly varying multipath, and large propagation delays, restrict the efficiency of UWA networks. Within such an environment, designing an UWA network that maximizes throughput and reliability while minimizing the power consumption becomes a very difficult task. The goal of this paper is to survey the existing network technology and its applicability to underwater acoustic channels. In addition, we present a shallow-water acoustic network example and outline some future research directions.

Mobile robot vision-based navigation has been the source of countless research contributions, from the domains of both vision and control. Vision is becoming more and more common in applications such as localization, automatic map construction, autonomous navigation, path following, inspection, monitoring or risky situation detection. This survey presents those pieces of work, from the nineties until nowadays, which constitute a wide progress in visual navigation techniques for land, aerial and autonomous underwater vehicles. The paper deals with two major approaches: map-based navigation and mapless navigation. Map-based navigation has been in turn subdivided in metric map-based navigation and topological map-based navigation. Our outline to mapless navigation includes reactive techniques based on qualitative characteristics extraction, appearance-based localization, optical flow, features tracking, plane ground detection/tracking, etc... The recent concept of visual sonar has also been revised.

Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site Highlights ► Societies increasingly depend on timely information on ecosystems and natural hazards. ► Data is needed to improve climate-related uncertainty and geo-hazard early warning. ► Observatory networks coordinate and integrate the collection of standardised data. ► Ocean observatories provide opportunity for ocean science to evolve.

Efficient path planning algorithms are a crucial issue for modern autonomous underwater vehicles. Classical path planning algorithms in artificial intelligence are not designed to deal with wide continuous environments prone to currents. We present a novel Fast Marching based approach to address the following issues. First, we develop an algorithm we call FM* to efficiently extract a continuous path from a discrete representation of the environment. Secondly we take underwater currents into account thanks to an anisotropic extension of the original Fast Marching algorithm. Thirdly, the vehicle turning radius is introduced as a constraint on the optimal path curvature for both isotropic and anisotropic medias. Finally, a multiresolution method is introduced to speed up the overall path planning process.

Multi-AUV operations have much to offer a variety of underwater applications. With sensors to measure the environment and coordination that is appropriate to critical spatial and temporal scales, the group can perform important tasks such as adaptive ocean sampling. We describe a methodology for cooperative control of multiple vehicles based on virtual bodies and artificial potentials (VBAP). This methodology allows for adaptable formation control and can be used for missions such as gradient climbing and feature tracking in an uncertain environment. We discuss our implementation on a fleet of autonomous underwater gliders and present results from sea trials in Monterey Bay in August 2003. These at-sea demonstrations were performed as part of the Autonomous Ocean Sampling Network (AOSN) II project.

This paper proposes a genetic algorithm (GA) for path planning of an autonomous underwater vehicle in an ocean environment characterized by strong currents and enhanced space-time variability. The goal is to find a safe path that takes the vehicle from its starting location to a mission-specified destination, minimizing the energy cost. The GA includes novel genetic operators that ensure the convergence to the global minimum even in cases where the structure (in space and time) of the current field implies the existence of different local minima. The performance of these operators is discussed. The proposed algorithm is suitable for situations in which the vehicle has to operate energy-exhaustive missions.

A Doppler compensation system is presented which is suitable for high-data-rate acoustic communication between rapidly moving platforms such as autonomous underwater vehicles. The proposed approach provides a generic preprocessor to conventional adaptive receiver structures with only a marginal increase in computational load and hardware cost. The preprocessor employs a novel Doppler estimation technique and efficient sample rate conversion to remove Doppler shift induced by platform velocity and acceleration. Performance predicted by simulation is compared to that of sea trials of a prototype communication system in the North Sea. Successful communication is demonstrated at 16 kbit/s with a transmitting platform moving at up to ± 2.6 m/s.

We have presented a decentralized control design methodology for regulating global functions of cooperating mobile systems. The application of relatively standard system-theoretic tools, leads to a novel broadcast-only communication structure. The feedback mechanism between vehicles is the measurement of the global variables and broadcast of their integrated values. More generally, the methods presented allow the designer to determine what explicit

The problem of controlling an Autonomous Underwater Vehicle (AUV) in a diving maneuver is addressed. The requirement for having a simple controller which performs satisfactorily in the presence of dynamical uncertainties calls for a design using the sliding mode approach, based on a dominant linear model and bounds on the nonlinear perturbations of the dynamics. Both nonadaptive and adaptive techniques are considered, leading to the design of robust controllers that can adjust to the changing dynamics and operating conditions. Also, the problem of using the observed state in the control design is addressed, leading to a sliding mode control system based on input-output signals in terms of dive-plane command and depth measurement. Numerical simulations using a full set of nonlinear equations of motion show the effectiveness of the proposed techniques.

A new type of control law is developed to steer an autonomous underwater vehicle (AUV) along a desired path. The methodology adopted for path-following deals explicitly with vehicle dynamics. Furthermore, it overcomes stringent initial condition constraints that are present in a number of path-following control strategies described in the literature. Controller design builds on Lyapunov theory and backstepping techniques. The resulting nonlinear feedback control law yields convergence of the path-following error trajectory to zero. Simulation results illustrate the performance of the control system proposed. r

Many underwater intervention tasks are today performed using manned submersibles or remotely operated vehicles in teleoperation mode. Autonomous underwater vehicles are mostly employed in survey applications. In fact, the low bandwidth and significant time delay inherent in acoustic subsea communications represent a considerable obstacle to remotely operate a manipulation system, making it impossible for remote controllers to react to problems in a timely manner.

Underwater acoustic networks are generally formed by acoustically connected ocean bottom sensor nodes, autonomous underwater vehicles (AUVs), and surface stations that serve as gateways and provide radio communication links to on-shore stations. The quality of service of such networks is limited by the low bandwidth of acoustic transmission channels, high latency resulting from the slow propagation of sound, and elevated noise levels in some environments. The long-term goal in the design of underwater acoustic networks is to provide for a self-configuring network of distributed nodes with network links that automatically adapt to the environment through selection of the optimum system parameters. This article considers several aspects in the design of shallow water acoustic networks that maximize throughput and reliability while minimizing power consumption.

This paper describes the implementation of an intelligent navigation system, based on the integrated use of the global positioning system (GPS) and several inertial navigation system (INS) sensors, for autonomous underwater vehicle (AUV) applications. A simple Kalman filter (SKF) and an extended Kalman filter (EKF) are proposed to be used subsequently to fuse the data from the INS sensors and to integrate them with the GPS data. The paper highlights the use of fuzzy logic techniques to the adaptation of the initial statistical assumption of both the SKF and EKF caused by possible changes in sensor noise characteristics. This adaptive mechanism is considered to be necessary as the SKF and EKF can only maintain their stability and performance when the algorithms contain the true sensor noise characteristics. In addition, fault detection and signal recovery algorithms during the fusion process to enhance the reliability of the navigation systems are also discussed herein. The proposed algorithms are implemented to real experimental data obtained from a series of AUV trials conducted by running the low-cost Hammerhead AUV, developed by the

A class of small (50 kg, 2 m length), reusable autonomous underwater vehicles capable of operating at speeds of 20-30 cm/s with ranges several thousand kilometers and durations of months has been developed and tested. The vehicles, essentially autonomous profiling floats with wings, execute sawtooth patterns between the surface, where they are located and communicate to shore, and depths of O(1000 m). These vehicles are commanded remotely and report results to shore in near real time. Different approaches to the various design issues are embodied in the vehicles Spray, Seaglider and Slocum models described here including battery-and thermal-powered propulsion, different depth and speed capabilities, diverse hydrodynamic approaches to reducing drag and gliding control, various communication schemes, and a range of sensors. These approaches are described and examples of completed scientific missions are given.

We have developed a visually based autopilot which is able to make an air vehicle automatically take off, cruise and land, while reacting appropriately to wind disturbances (head wind and tail wind). This autopilot consists of a visual control system that adjusts the thrust so as to keep the downward optic flow (OF) at a constant value. This autopilot is therefore based on an optic flow regulation loop. It makes use of a sensor, which is known as an elementary motion detector (EMD). The functional structure of this EMD was inspired by that of the housefly, which was previously investigated at our Laboratory by performing electrophysiological recordings while applying optical microstimuli to single photoreceptor cells of the insect's compound eye.

This paper presents a six-degrees-of-freedom controller for autonomous underwater vehicles. The control algorithm is adaptive in the dynamic parameters that are poorly known and time-varying in the underwater environment. Moreover, the proposed control law adopts quaternions to represent attitude errors, and thus avoids representation singularities that occur when using instead Euler angles description of the orientation. The adaptive controller has been successfully implemented and experimentally validated on omni-directional intelligent navigator (ODIN), an autonomous underwater vehicle that has been designed and built at the University of Hawaii. The experimental results demonstrate the good performance of the proposed controller within the constraints of the sensory system.

This paper outlines the specifications and basic design approach taken on the development of the Girona 500, an autonomous underwater vehicle whose most remarkable characteristic is its capacity to reconfigure for different tasks. The capabilities of this new vehicle range from different forms of seafloor survey to inspection and intervention tasks.

Path planning and trajectory design for autonomous underwater vehicles (AUVs) is of great importance to the oceanographic research community because automated data collection is becoming more prevalent. Intelligent planning is required to maneuver a vehicle to high-valued locations to perform data collection. In this paper, we present algorithms that determine paths for AUVs to track evolving features of interest in the ocean by considering the output of predictive ocean models. While traversing the computed path, the vehicle provides near-real-time, in situ measurements back to the model, with the intent to increase the skill of future predictions in the local region. The results presented here extend prelim- inary developments of the path planning portion of an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. This extension is the incorporation of multiple vehicles to track the centroid and the boundary of the extent of a feature of interest. Similar algorithms to those presented here are under development to consider additional locations for multiple types of features. The primary focus here is on algorithm development utilizing model predictions to assist in solving the motion planning problem of steering an AUV to high-valued locations, with respect to the data desired. We discuss the design technique to generate the paths, present simulation results and provide experimental data from field deployments for tracking dynamic features by use of an AUV in the Southern California coastal ocean.

The paper presents a solution to the problem of combined trajectory tracking and path following system design for autonomous marine craft. This problem is motivated by the practical need to develop control systems for marine craft that can yield good trajectory tracking performance while keeping some of the desired properties normally associated with path following. The solution described builds on and extends previous work by Hindman and Hauser on so-called maneuver modi ed trajectory tracking. An application is made to the problem of designing a control system for the coordinated operation of an autonomous surface craft (ASC) and an autonomous underwater vehicle (AUV). Simulations with nonlinear models of an underactuated marine craft and a fully actuated underwater vehicle illustrate the performance of the control system derived.

Ocean processes are dynamic, complex, and occur on multiple spatial and temporal scales. To obtain a synoptic view of such processes, ocean scientists collect data over long time pe- riods. Historically, measurements were continually provided by fixed sensors, e.g., moorings, or gathered from ships. Recently, an increase in the utilization of autonomous underwater vehicles has enabled a more dynamic data acquisition approach. However, we still do not utilize the full capabilities of these vehicles. Here we present algorithms that produce per- sistent monitoring missions for underwater vehicles by balancing path following accuracy and sampling resolution for a given region of interest, which addresses a pressing need among ocean scientists to efficiently and effectively collect high-value data.
More specifically, this paper proposes a path planning algorithm and a speed control algo- rithm for underwater gliders, which together give informative trajectories for the glider to persistently monitor a patch of ocean. We optimize a cost function that blends two com- peting factors: maximize the information value along the path, while minimizing deviation from the planned path due to ocean currents. Speed is controlled along the planned path by adjusting the pitch angle of the underwater glider, so that higher resolution samples are collected in areas of higher information value. The resulting paths are closed circuits that can be repeatedly traversed to collect long-term ocean data in dynamic environments. The algorithms were tested during sea trials on an underwater glider operating off the coast of southern California, as well as in Monterey Bay, California. The experimental results show significant improvements in data resolution and path reliability compared to previously ex- ecuted sampling paths used in the respective regions.

Future underwater vehicles will be increasingly called upon to work in cluttered environments and to interact with their surroundings. These vehicles will need sensors that work efficiently at short range and be highly maneuverable at low speed. To obtain insights into principles and mechanisms of low-speed operation in cluttered environments, we examine a fish that excels in this regime, the black ghost knifefish Apteronotus albifrons. This fish hunts in dark or turbid water using a short-range self-generated electric field to sense its surroundings. Coupled with this unique mode of sensing is an unusual ribbon fin propulsion system that confers high multidirectional maneuverability at low speeds. To better understand the relationship between body morphology and common maneuvers of this fish, we utilized an idealized ellipsoidal body model, Kirchhoff's equations, and an optimal control algorithm for generating trajectories. We present evidence that common fish trajectories are optimal, and that these trajectories complement the sensory abilities of the fish. We also discuss prototypes of the sensing and propulsion systems of the fish with a view to providing alternative approaches for underwater vehicle design where high maneuverability in geometrically complex environments is needed.

This paper derives a control law to steer the dynamic model of an autonomous underwater vehicle (AUV) along a desired path. The methodology adopted for path following deals explicitly with vehicle dynamics. Furthermore, it overcomes stringent initial condition constraints that are present in a number of path following control strategies described in the literature. Controller design builds on Lyapunov theory and backstepping techniques. The resulting nonlinear feedback control law yields convergence of the path following error trajectories to zero. Simulation results illustrate the performance of the control system proposed.

In this paper, we examine the issues associated with docking autonomous underwater vehicles (AUVs) operating within an Autonomous Ocean Sampling Network (AOSN). We present a system based upon an acoustic ultrashort baseline system that allows the AUV to approach the dock from any direction. A passive latch on the AUV and a pole on the dock accomplish the task of mechanically docking the vehicle. We show that our technique for homing is extremely robust in the face of the two dominant sources of error-namely the presence of currents and the presence of magnetic anomalies. Our strategy for homing is independent of the initial bearing of the dock to the AUV, includes a method for detecting when the vehicle has missed the dock, and automatically ensures that the AUV is in a position to retry homing with a greater chance of success. Our approach is seen to be extremely successful in homing the vehicle to the dock, mechanically attaching itself to the dock, aligning inductive cores for data and power transfer, and undocking at the start of a fresh mission. Once the AUV is on the dock, we present a methodology that allows us to achieve the complex tasks with ensuring that the AUV is securely docked, periodically checking vehicle status, reacting to a vehicle that requires charging, tracking it when it is out on a mission, archiving and transmitting via satellite the data that the AUV collects during its missions, as well as providing a mechanism for researchers removed from the site to learn about vehicle status and command high-level missions. The dock is capable of long-term deployments at a remote site while respecting the constraints-low power, small size, low computational energy, low bandwidth, and little or no user input-imposed by the amalgamation of acoustic, electronic and mechanical components that comprise the entire system. Index Terms-Acoustic ultrashort baseline system, autonomous underwater vehicles, AUV docking, homing strategies.

We examine the problem of utilizing an autonomous underwater vehicle (AUV) to collect data from an underwater sensor network. The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication. The AUV must plan a path that maximizes the information collected while minimizing travel time or fuel expenditure. We propose AUV path planning methods that extend algorithms for variants of the Traveling Salesperson Problem (TSP). While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multi-node communication requires a scheduling protocol that is robust to channel variations and interference. To this end, we examine two multiple access protocols for the underwater data collection scenario, one based on deterministic access and another based on random access. We compare the proposed algorithms to baseline strategies through simulated experiments that utilize models derived from experimental test data. Our results demonstrate that properly designed communication models and scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.

The paper considers feedback stabilization of the position and attitude of an autonomous underwater vehicle (AUV) with a reduced number of actuators. A nonlinear model describing both the dynamics and the kinematics of an AUV is studied. The paper shows that previous results on attitude stabilization of a spacecraft can be applied to exponentially stabilize both the position and attitude of an AUV using only four, possibly three, actuators. Simulation results are presented. Corresponding author to limit the number of actuators due to e.g. cost and weight considerations. One class of AUVs has available control force in surge and control torques in roll, pitch and yaw, but no actuators in sway and heave. Another interesting thruster con guration is when, in addition, roll control torque is not available. Control of the AUVs with three and four control inputs was studied in 1, 2, 3]. These works considered a kinematic, drift free model of an AUV, where the velocities were the control inputs, and certain velocities were constrained to zero. However, due to hydrodynamic, centripetal and gravitational forces and torques, AUVs will generally have non-constant velocities even if an element of the force and torque control vector is zero. It is therefore of interest to consider the dynamics in the control system design. 4] was the rst to consider a dynamic AUV model with force and torque control inputs. It was shown in 4] how open-loop periodic time-varying control can be used to reposition and reorient underactuated AUVs.

This paper addresses the problems of automatically planning Autonomous Underwater Vehicle (AUV) paths which best exploit complex current data, from computational estuarine model forecasts, while also avoiding obstacles. In particular we examine the possibilities for a novel type of AUV mission deployment in fast flowing tidal river regions which experience bi-directional current flow. These environments are interesting in that, by choosing an appropriate path in space and time, an AUV may both bypass adverse currents which are too fast to be overcome by the vehicle's motors and also exploit favorable currents to achieve far greater speeds than the motors could otherwise provide, while substantially saving energy. The AUV can "ride" currents both up and down the river, enabling extended monitoring of otherwise energy-exhausting, fast flow environments. The paper discusses suitable path parameterizations, cost functions and optimization techniques which enable optimal AUV paths to be efficiently generated. These paths take maximum advantage of the river currents in order to minimize energy expenditure, journey time and other cost parameters. The resulting path planner can automatically suggest useful alternative mission start and end times and locations to those specified by the user. Examples are presented for navigation in a simple simulation of the fast flowing Hudson River waters around Manhattan.

Four-dimensional variational assimilation of water vapor differential absorption lidar (DIAL) data has been applied for investigating their impact on the initial water field for mesoscale weather forecasting. A case that was observed during the International H 2 O Project (IHOP_2002) has been selected. During 24 May 2002, data from the NASA Lidar Atmospheric Sensing Experiment were available upstream of a convective system that formed later along the dryline and a cold front. Tools were developed for routinely assimilating water vapor DIAL data into the fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5). The results demonstrate a large impact on the initial water vapor field. This is due to the high resolution and accuracy of DIAL data making the observation of the high spatial variability of humidity in the region of the dryline and of the cold front possible. The water vapor field is mainly adjusted by a modification of the atmospheric wind field changing the moisture transport. A positive impact of the improved initial fields on the spatial/temporal prediction of convective initiation is visible. The results demonstrate the high value of accurate, vertically resolved mesoscale water vapor observations and advanced data assimilation systems for short-range weather forecasting.

An autonomous network of underwater vehicles is considered in which there is no central node, but the vehicles communicate in a distributed manner over multiple hops. The focus of network d esign is on a scalable multiple access technique which is applicable to varying coverage areas as well as varying number of vehicles. The proposed scheme relies on grouping the adjacent vehicles into clusters, and using time -division multiple access within each cluster. Interference among clusters is managed by assigning different spreading codes to adjacent clusters, while scalability is achieved by spatial reuse of the codes. Network operation begins with an initialization phase, and moves on to continuous maintenance during which mobility is managed. Performance is quantified through measures of connectivity, successful transmission rate, average delay and energy consumption. Simulation analysis is used to obtain optimal cluster size and transmission power for a network with given density of vehicles.

The paper describes the current developments of a class of low-cost, light-weight autonomous underwater vehicles for coastal oceanographic applications; the vehicle class is named Fòlaga, the Italian name of an aquatic bird that swims on the water surface and dives to catch fish. The main design characteristics of the most recent vehicle of the class, the Fò laga III, are reviewed. Navigation and control system design are discussed, with particular attention to the diving phase, which is accomplished as in oceanographic gliders by varying the vehicle buoyancy and attitude. Experimental results show that the PID robust controllers implemented are effective in the diving control phase. Finally, a distributed cooperation algorithm to be applied by a team of Fò laga-like vehicles in adaptive oceanographic sampling applications is described. The algorithm optimizes area coverage while taking into account the accuracy in the reconstruction of the oceanographic field and inter-vehicle communication through a range constraint. The resulting dynamic programming algorithm can be implemented in a distributed fashion among the team components.

1] To make progress in understanding the distribution and genesis of coral mounds in cold and dark water, maps of morphology and oceanographic conditions resolving features at the 1 -10 m scale are needed. An autonomous underwater vehicle (AUV) cruising 40 m above the seafloor surveyed a 48 km 2 coral mound field in 600-800 m water depth at the base of slope of Great Bahama Bank. The AUV acquired 1 -3 meter resolution acoustic backscatter and bathymetry together with current vectors, salinity, and temperature. The multibeam bathymetry resolved more than 200 coral mounds reaching up to 90 m height. Mound morphology is surprisingly diverse and mound distribution follows E-W oriented off-bank ridges. Bottom currents reverse every 6 hours indicating tidal flow decoupled from the north flowing surface current. The AUV data fill the gap between low-resolution surface-based mapping and visual observations on the seafloor, revealing the dynamic environment and spatial relationships of an entire coral mound field. Citation: Grasmueck, M., G. P. Eberli, D. A.

The design and construction of a biomimetic flapping foil autonomous underwater vehicle is detailed. The vehicle was designed as a proof of concept for the use of oscillating foils as the sole source of motive power for a cruising and hovering underwater vehicle. Primary vehicle design requirements included scalability and flexibility in terms of the number and placement of foils, so as to maximize experimental functionality. This goal was met by designing an independent self-contained module to house each foil, requiring only direct current power and a connection to the vehicle's Ethernet local area network for operation. The results of tests on the foil modules in the Massachusetts Institute of Technology (MIT) Marine Hydrodynamics Water Tunnel and the MIT Ship Model Testing Tank are both used to demonstrate fundamental properties of flapping foils and to predict the performance of the specific vehicle design based on the limits of the actuators. The maximum speed of the vehicle is estimated based on the limitations of the specific actuator and is shown to be a strong function of the vehicle drag coefficient. When using four foils, the maximum speed increases from 1 m/s with a vehicle of 1.4 to 2 m/s when = 0 1, where is based on vehicle frontal area. Finally, issues of vehicle control are considered, including the decoupling of speed and pitch control using pitch-biased maneuvering and the tradeoff between actuator bandwidth and authority during both the cruising and hovering operation.

Autonomous Underwater Vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of sub sea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of Reinforcement Learning Direct Policy Search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task.

A new motion coordination algorithm for an autonomous underwater vehicle-manipulator system (UVMS) is proposed. This algorithm generates the desired trajectories for both the vehicle and the manipulator in such a way that the total hydrodynamic drag on the system is minimized. Resolution of kinematic redundancy of the system is performed at the acceleration level so that this algorithm can be incorporated into the system dynamics. The dynamics of the UVMS are modeled using a quasi-Lagrange approach. A state-space formulation of the system along with a model-based controller design for trajectory-following tasks that includes thruster dynamics is also presented. The computer simulation results demonstrate the effectiveness of this proposed method in reducing the drag on the system. Index Terms-Drag optimization, kinematically redundant system, underwater vehicle-manipulator system.

Ship hulls, as well as bridges, port dock pilings, dams, and various underwater structures need to be inspected for periodic maintenance. Additionally, there is a critical need to provide protection against sabotage activities, and to establish effective countermeasure against illegal smuggling activities. Unmanned underwater vehicles are suitable platforms for the development of automated inspection systems, but require integration with appropriate sensor technologies. This paper describes a vision system for automated ship inspection, based on computing the necessary information for positioning, navigation and mapping of the hull from stereo images. Binocular cues are critical in resolving a number of complex visual artifacts that hamper monocular vision in shallow water conditions. Furthermore, they simplify the estimation of vehicle pose and motion which is fundamental for successful automatic operation. The system has been implemented on a commercial ROV, and tested in pool and dock tests. Results from various trials are presented to demonstrate the system capabilities.

This paper describes an on-going research effort to achieve real-time cooperative localization of multiple autonomous underwater vehicles. We describe a series of experiments that utilize autonomous surface craft (ASC), equiped with undersea acoustic modems, GPS, and 802.11b wireless ethernet communications, to acquire data and develop software for cooperative localization of distributed vehicle networks. Our experiments demonstrate the capability of the Woods Hole acoustic modems to provide accurate round-trip and one-way range measurements, as well as data transfer, for a fully mobile network of vehicles in formation flight. Finally, we present preliminary results from initial experiments involving cooperative operation of an Odyssey III AUV and two ASCs, demonstrating ranging and data transfer from the ASCs to the Odyssey III.

This paper addresses localization of autonomous underwater vehicles (AUVs) from acoustic time-of-flight measurements received by a field of surface floating buoys. It is assumed that measurements are corrupted by unknown-but-bounded errors, with known bounds. The localization problem is tackled in a set-membership framework and an algorithm is presented, which produces as output the set of admissible AUV positions in a three-dimensional (3-D) space. The algorithm is tailored for a shallow water situation (water depth less than 500 m), and accounts for realistic variations of the sound speed profile in sea water. The approach is validated by simulations in which uncertainty models have been obtained from field data at sea. Localization performance of the algorithm are shown comparable with those previously reported in the literature by other approaches who assume knowledge of the statistics of measurement uncertainties. Moreover, guaranteed uncertainty regions associated to nominal position estimates are provided. The proposed algorithms can be used as a viable alternative to more traditional approaches in realistic at-sea conditions.

In this paper three-dimensional finite element method analysis and experimental flashless cold forging of aluminium front and back hubs, and the blade of an autonomous underwater vehicle propeller are presented. The rigid-plastic finite element simulation is performed using Deform F-3V6.0, to estimate the optimum load required for the flashless cold forging. The complex profiles of the hubs and blade are modelled using Solidworks SP4.02007, which is also used for the modelling of the workpiece and die-punch assembly. The workpiece used is of AISI AL6061 and the die material is die steel (AISI D2). The process is optimized to form the propeller back and front hubs, and the blade. For all the models, three workpieces with different specifications are selected and investigated to obtain the optimum workpiece that gives flashless cold forging with no underfilling. Based on the simulation results, the flashless cold forging is successfully done on a 100 tonne C-type machine. The experimental forged samples conform well with the simulated models.

Autonomous underwater vehicles, and in particular autonomous underwater gliders, represent a rapidly maturing technology with a large cost-saving potential over current ocean sampling technologies for sustained (month at a time) real-time measurements. In this paper we give an overview of the main building blocks of an underwater glider system for propulsion, control, communication and sensing. A typical glider operation, consisting of deployment, planning, monitoring and recovery will be described using the 2003 AOSN-II field experiment in Monterey Bay, California. We briefly describe recent developments at NRC-IOT, in particular the development of a laboratory-scale glider for dynamics and control research and the concept of a regional ocean observation system using underwater gliders.