This paper will present research results from the Adaptive Autonomous Ocean Sampling Networks (AAOSN) project; a £1.5million project funded by NERC, Dstl and the Technology Strategy Board, to develop new technology solutions for coordinating a suite of marine autonomous systems (MAS). The SeeByte consortium, which includes ASV (a leading marine autonomous systems manufacturer) and the Marine Biological Association of the UK, aims to provide an open software tool-set and user interface that will enable improved use of a wide-range of autonomous systems in gathering data from the ocean over extended periods, while constantly adapting to the environment and mission requirements. The software framework has been designed so that 3 rd parties are able to write behaviors. The conclusion from the in-water test missions was that the autonomy behavior, developed by ASV, can help ensure high utilization of the autonomous systems while decreasing direct operator piloting. However, the complete system remains to be tested as part of a longer trial that also includes the controlled release of tagged fish in an appropriate water management zone. It is hoped that this will occur within the next 12 months and will prove a significant step forward in survey methods for EU Habitat Directive management zones.

This paper will present research results from the Adaptive Autonomous Ocean Sampling Networks (AAOSN) project, which is a £1.5million project funded in the UK by NERC, Dstl and the Technology Strategy Board, to develop new technology solutions for coordinating a suite of Marine Autonomous Systems (MAS). The SeeByte consortium, which comprises ASV and Marine Biological Society of the UK, aims to provide an open software tool-set and user interface that will enable improved use of a wide-range of autonomous systems in gathering data from the ocean over extended periods, while constantly adapting to the environment and mission requirements. The AAOSN project targeted a range of applications for mixed MAS fleets, including tagged fish tracking, tidal mixing fronts and swath bathymetry mapping. The work presented in this paper was targeted at tracking Tidal Mixing Fronts and focuses on the stress testing of the system concepts through a comprehensive Virtual Experiment constructed using NOC large gridded ocean model datasets and other high fidelity simulators to enable 30-days of tidal cycle to be tested over a 5-day period.

SeeByte have developed a suite of tools to enable command and control of off-board autonomous systems. This includes SeeTrack, a top-side mission tool suit, and Neptune, a multi-vehicle autonomy system. The application of these tools, with a particular focus on in-mission adaptive and autonomous behaviors, is being investigated for use in oceanographic and environmental research scenarios. “Autonomous systems have the potential to revolutionize the conduct of maritime and amphibious warfare. This transformation could be as dramatic as the move from sail to steam, the invention of the submarine or the advent of naval aviation” First Sea Lord, Royal Navy. This transformative potential also applies to a wide range of maritime activities, including hydrography, oceanography and environmental research. However, it is clear that to gain the best advantages for scientific research the latest autonomous systems and technology needs to be made available to a wider pool of users. In particular, the current quantity and quality of data available to scientific research is limited in many aspects. The overarching aim for the delivered software framework and the scenario behaviors will be to enable maritime autonomous systems to be the “force multiplier” required. Particular focus be given to ensure that the behaviors developed are capable reducing the frequency and complexity of operator input and enabling shore-based operations with very long duration missions. This paper will present two on-going pieces of work, giving some insight in to the technology developed and some initial results from the programs. The first is the Adaptive Autonomous Ocean Sampling Networks (AAOSN) project, which is a £1.5million project run NERC, Dstl and the Innovate UK, to develop new technology solutions for coordinating a suite of Marine Autonomous Systems (MAS) enabling tracking of dynamic features. The SeeByte consortium, which comprises ASV and Marine Biological Society the UK, aims to provide an open software tool-set and user interface that will enable improved use of a wide-range of autonomous systems in gathering data from the ocean over extended periods, while constantly adapting to the environment and mission requirements. The behaviors will integrate sensor data and interpretation methods to enable adaptive, multi-vehicle missions using combinations of USV, UUV and Glider assets. Particular focus be on extending the existing work on Tagged Fish Tracking, to develop a multi-vehicle approach with a ten-day in-water field-trial occurring in the Plymouth Sound. The second program of work discussed is development of a SeeTrack glider-fleet management tool that can remotely monitor the position of each glider in the fleet and re-plan and change mission plans accordingly. This effectively provides a tool where every glider's vital stats can be monitored and tasking updated in one place. This can be done from a remote laptop at any time of day. This tool was originally developed for the NOC MARS (Maritime Autonomous Remote Systems) team and will provide remote mission planning capabilities, as well as enabling 24/7 remote monitoring for the UK National Facilities glider fleet managed by NOC.

Remotely Operated Vehicles (ROVs) now offer sonar-based control that enable autonomous inspection capabilities at stand-off distances greater than the blanking range of the sonar. At near ranges, equivalent autonomy behavior must be provided using on-board video sensors. This paper presents a sensor-based servo capability where a suite of onboard Automatic Target Recognition (ATR) systems fuse input from multiple sensors (sonar and video) to provide the required input to the control system, allowing the ROV to maintain station relative to an object of interest. The video ATR is then presented; this is trained in-mission, leveraging the ROV user interface and high communication bandwidth to allow the operator to select and provide training data to the ATR. The system allows the ROV autonomy to be deployed against a priori unknown targets selected by the pilot. The video ATR uses a Fern Classifier in conjunction with an Optical Flow based tracker to allow the ATR to cope with variations in the target appearance over time. SeeByte's ROV Dynamic Positioning product, SeeTrack Copilot, is then presented before discussing the integration of these technologies to provide a novel, end-to-end solution for ROV auto-transit, dynamic positioning and visual servoing. Results are presented using the VideoRay Pro4 micro-ROV platform. The demonstrated prototype technology offers potential as a diver replacement in applications as varied as Mine Counter Measures and Underwater Archeology.

Abstract A new tool for subsea inspection in the offshore oil and gas industry iscurrently going through performance and qualification testing. The AutonomousInspection Vehicle (AIV) has been designed and built by Subsea7 and SeeByte Ltdto provide the industry with a valuable tool capable of making a positivecontribution to Life of Field operations. The station keeping and hovering ability of the AIV is the next step in theevolution of autonomous systems in the marine environment. Survey classautonomous vehicles have already shown their value with improved data qualityand efficiencies over traditional methods. The first commercial AIV will becapable of many of the inspection tasks currently carried out by RemotelyOperated Vehicles (ROV). Regular inspection data of risers, pipelines andseabed equipment can be gathered using a single AIV operating directly from anoffshore facility. A more rapid assessment of a field can be made usingmultiple systems operating together from a single support vessel. This has notbeen done before with a commercial vehicle and hence is truly leading edgetechnology. The paper will outline some of the technical challenges in creating the vehicleand how the use of advanced simulation linked to practical testing is beingused to ensure the performance of the system. Finally the paper will draw aparallel with the evolution of subsea infrastructure that has fully enabled thecapability of the ROV and demonstrate how the introduction of autonomoustechnology should be considered with confidence. Introduction Subsea 7 is a major engineering and construction company supplying a range ofservices and technologies primarily into the offshore oil and gas industry. Thecompany operates a worldwide fleet of high specification subsea constructionand Inspection vessels together with over 100 ROVs. The company is activelyinvolved in bringing new and innovative technologies to the market to supportits operational focus. The development of autonomous underwater vehicles is onesuch technology. SeeByte is a cutting-edge software company that develops some of the world'smost advanced software for managing unmanned and remote assets. The company isfocused on advancing autonomous vehicle development and is actively involved inmajor programs of work for international customers. SeeByte brings a proventrack record of de-risking and demonstrating this technology for customers inmilitary, offshore and subsea sectors.

Abstract Ongoing development work by Subsea 7 and SeeByte has further demonstrated that the use of an Autonomous Underwater Vehicle (AUV) as an inspection and intervention tool for the offshore oil and gas industry will soon become a reality. The paper will describe the development work to-date, in particular the physical form of the Autonomous Inspection Vehicle (AIV) under development, which differs greatly from the more common torpedo profiles. The modes of operation for the AIV will be highlighted including the operation of numerous systems deployed simultaneously. The paper will outline how the AIV concept can be applied in a number of Life of Field operational scenarios as a viable tool capable of making a positive contribution to the industry. Integration of the AIV with other ongoing technology developments will also provide a thought provoking view of what may lie ahead. Finally the paper will draw a parallel with the evolution of the Remotely Operated Vehicle (ROV) demonstrate how the AIV's introduction and full potential should be considered with confidence. Introduction Subsea 7 is a major engineering and construction company supplying a range of services and technologies primarily into the offshore oil and gas industry. The company operates a worldwide fleet of high specification subsea construction and survey vessels together with over 100 ROVs. Over the past ten years Subsea 7 has been actively involved in the development of autonomous underwater technologies. SeeByte is a cutting-edge software company with a track record in delivering solutions to both military and industry customers helping them automate and de-risk their processes. Through its involvement in key programs of work, SeeByte has been at the forefront of AUV development. As offshore oil and gas exploration and production has evolved over the last decades more and more infrastructure is being installed on the seabed, of increasing complexity, in deeper water and of increased criticality to successful operations. Installation, support & maintenance of this equipment is currently carried out using specialized vessel based ROVs and/or diving operations. Subsea 7's and SeeByte's vision is to use our know-how in offshore operations and autonomous technology to provide this type of service using a hover-capable inspection AUV operating from a host facility. The ability to operate directly from the host facility provides significant advantages as routine or unplanned inspections can be easily and frequently carried out without a dedicated infield support vessel. Initial development was based on a torpedo shaped AUV to help develop intelligent payload systems such as SeeByte's " Autotracker??. This payload is used to analyze the side-scan and multibeam data in real time to detect and track pipelines. The output from the tracker is fused with the legacy data and is used to plan an optimal route for the AUV to survey the pipeline. Subsea 7's engineering know-how was also of service to SeeByte when developing the SpiNav program, a payload for AUV riser inspection. Through these programs it became evident that the technology was ready for transition to a hover-capable AUV capable of carrying out inspection and eventually light intervention work. This paper is focused on inspection and intervention by means of a hover capable AUV, the AIV.

1.1 Trajectory planning This chapter is a contribution to the field of Artificial Intelligence. Artificial Intelligence can be defined as the study of methods by which a computer can simulate aspects of human intelligence (Moravec, 2003). Among many mental capabilities, a human being is able to find his own path in a given environment and to optimize it according to the situation requirements. For an autonomous mobile robot, the computation of a safe trajectory is crucial for the success of a mission. Here is the ultimate goal of the trajectory planning issue for autonomous robots:

This paper presents AUTOTRACKER, an autonomous pipeline inspection system that operates as a dynamic mission payload for an Autonomous Underwater Vehicle (AUV). The paper describes the mode of operation, together with the validation & trial operations AUTOTRACKER has undertaken over the years, and how this valuable experience has been fed back into the future development of the system:

We present the design and evaluation of an architecture for collision avoidance and escape of mobile autonomous robots operating in unstructured environments. The approach mixes both reactive and deliberative components. This provides the vehicle's behavior designers with an explicit means to design-in avoidance strategies that match system requirements in concepts of operations and for robot certification. The now traditional three layer architecture is extended to include a fourth Scenario layer, where scripts describing specific responses are selected and parameterized on the fly. A local map is maintained using available sensor data, and adjacent objects are combined as they are observed. This has been observed to create safer trajectories. Objects have persistence and fade if not re-observed over time. In common with behavior based approaches, a reactive layer is maintained containing pre-defined knee jerk responses for extreme situations. The reactive layer can inhibit outputs from above. Path planning of updated goal point outputs from the Scenario layer is performed using a fast marching method made more efficient through lifelong planning techniques. The architecture is applied to applications with Autonomous Underwater Vehicles. Both simulated and open water tests are carried out to establish the performance and usefulness of the approach.

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 (FM)-based approach to address the following issues. First, we develop an algorithm we call FM* to efficiently extract a 2-D continuous path from a discrete representation of the environment. Second, we take underwater currents into account thanks to an anisotropic extension of the original FM algorithm. Third, the vehicle turning radius is introduced as a constraint on the optimal path curvature for both isotropic and anisotropic media. Finally, a multiresolution method is introduced to speed up the overall path-planning process.

This study considers the problem of constructing a decentralised, distributed world model to enable more effective cooperation of multiple auton omous vehicles, whilst retaining the capability for isolated operation. A brief literatu re review highlighted the immature status of this area of the field, and a new architecture was proposed employing semantic web ontologies. This significantly improves on the soph istication and capabilities of the models used in existing works, whilst remaining generic an d portable. A prototype implementation is now proceeding, and will be evaluated using simulat ed and real assets of the Ocean Systems Laboratory in cooperative underwater vehicle missio ns, such as mine counter measures.

Autonomy for autonomous vehicles is achieved at the level of decision making. This paper discusses the lack of autonomous decision making in current unmanned vehicles platforms. Although current unmanned vehicles are often term aautonomous, they relay on the communications and decisions established with and boperator. In order to achieve this autonomy during mission without requiring operator feedback, the study proposes the integration of novel approaches in the context of o n-line plan repair algorithms capable of providing machine behavioural awareness for on-line mission adaptation and recoverability.

This research addresses the problem of coordinating multiple autonomous underwater vehicle (AUV) operations. An architecture has been created that uses multi-agent technology to control and coordinate multiple AUVs in communication deficient environments. By incorporating a simple broadcast communication system in conjunction with real time vehicle prediction this architecture can handle the limitations inherent in underwater operations and intelligently control multiple vehicles. In this research efficiency in terms of mission speed, battery life, communication robustness and goal redundancy is evaluated and then compared to the current state of the art in multiple AUV control.

The architecture of an advanced fault detection and diagnosis (FDD) system is described and applied with an Autonomous Underwater Vehicle (AUV). The architecture aims to provide a more capable system that does not require dedicated sensors for each fault, can diagnose previously unforeseen failures and failures with cause-effect patterns across different subsystems. It also lays the foundations for incipient fault detection and condition-based maintenance schemes. A model of relationships is used as an ontology to describe the connected set of electrical, mechanical, hydraulic, and computing components that make up the vehicle, down to the level of least replaceable unit in the field. The architecture uses a variety of domain dependent diagnostic tools (rulebase, model-based methods) and domain independent tools (correlator, topology analyzer, watcher) to first detect and then diagnose the location of faults. Tools nominate components, so that a rank order of most likely candidates can be generated. This modular approach allows existing proven FDD methods (e.g., vibration analysis, FMEA) to be incorporated and to add confidence to the conclusions. Illustrative performance is provided working in real time during deployments with the RAUVER hover capable AUV as an example of the class of automated system to which this approach is applicable. © 2007 Wiley Periodicals, Inc.

The force-multiplier advantages of multi-vehicle operations are well known, but to achieve the full benefits, suitable architectures that facilitate true cooperative autonomous planning must be realised. This study aims to review potential approaches from the existing literature on cooperative robotics, and through prototyping on real UxVs, de-risk the transition and future development of suitable technologies.

Autonomous decision making for system recoverability from failures or damage is a key challenge for next generation unmanned vehicles (UXVs). Architectures capable of coping with unforeseen situations during mission time are a desirable technology that can undoubtedly improve vehicle operability. This paper presents the work that has been carried out in order to research the current-state-of-the-art and existing technologies for the development of such systems. The paper describes some findings, presents an architecture study and proposes some recommendations for the ongoing work.

The force-multiplier advantages of multi-vehicle operations are well known, but to achieve the full benefits, suitable architectures that facilitate true cooperative autonomous planning must be realised. This study aims to review potential approaches from the existing literature on cooperative robotics, and through prototyping on real UxVs, de-risk the transition and future development of suitable technologies.