Project abstracts (not edited) of winners of Phase 1 of the SBRI call on Adaptive Autonomous Ocean Sampling Networks

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1 Project abstracts (not edited) of winners of Phase 1 of the SBRI call on Adaptive Autonomous Ocean Sampling Networks NOC-DSTL-Innovate UK SBRI team Version 1: SBRI NOC-AAOSN Extending Dstl s UK Maritime Autonomy Framework to Oceanographic and Scientific Research Applications - Seebyte This project will extend the UK Maritime Autonomy Framework (MAF), that is currently being used in a range of Dstl programmes, to provide an unmanned vehicle (UxV) mission management system for a range of oceanographic and environmental scenarios required for ongoing scientific research. The system builds on proven technologies and capabilities from both SeeByte and ASV to provide a reliable solution for AOSN. Phase 1 of the project will provide proof-of-concept demonstrations of two of the Scenarios outlined in the AOSN call, and will include one in-water demonstration. Phase 1 will also provide a detailed design for the extension of UK MAF to AOSN and will show some key technologies that will be applicable to a wide range of AOSN scenarios. The overall aim of this technical approach is to provide an open software tool-set and user interface that will enable improved use of autonomous systems in gathering data from the ocean over extended periods, while constantly adapting to the environment and mission requirements. Autonomous systems have the potential to revolutionise the conduct of maritime and amphiobous 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. It is clear that to gain the best advantages for scientific research the latest autonomous systems and technology needs to be made available.

2 SBRI NOC-AAOSN A Framework for Sampling Ocean Features Using Maritime Autonomous Systems University of Exeter Novel decision making schemes, developed at the University of Exeter, to track hotspots (local maxima) and to determine and track the boundary (level set) of spatial phenomena will be adapted to enable multiple marine autonomous systems (MAS) for a wide class of adaptive autonomous ocean sampling problems in particular the detection and mapping of ocean fronts. The methods will be tested and validated in a series of experiments using simulated ocean properties supplied by the Met Office. The project is led by the University of Exeter who have developed different high level guidance algorithms for monitoring the spread of contaminants in the environment (for e.g., volcanic ash) which includes mapping of the boundaries and determining the sources/hot spots. Representative simple kinematic models, which are widely used in the literature, have been used to represent the steering behaviour of the autonomous systems. These guidance methods, comprising novel contour following algorithms and cooperative and distributed swarm intelligence algorithms for specific purposes, were originally developed at Exeter as part of the UK India Education Research Initiative (UKIERI) project, entitled Contamination Monitoring using Multiple Mobile Sensor Networks. These will be extended to a Marine Autonomy Framework (MAF), a guidance decision making framework supporting the integration of high level autonomous vehicle kinematic behaviours and planning algorithms. In the future, this will be interfaced with the low level control schemes of the Marine Autonomous Systems. For this project the University of Exeter has partnered with the Met Office. The Met Office will provide access to large-scale and detailed ocean models (North West Atlantic Shelf) assimilated from a wide range of data sources. Information from this large-scale model will be used as a prior for a more detailed model of a particular oceanic feature. The University of Exeter will provide an oceanic feature model that will be used to identify and plan. The oceanic feature model will be probabilistic to enable a set of MAS plans to be generated that maximise the amount of information gained about the feature under investigation by optimally manoeuvring the MAS. The key outcome of this project will be a demonstration of the feasibility of this end-to-end system and to show how the currently developed novel algorithms can interface with an emulator environment and thus how these methodologies could benefit source seeking and boundary mapping applications in Phase 2.

3 SBRI NOC-AAOSN-207 OSMOS Ocean Sensing Mission Optimisation System RED Scientific Ltd. Ocean Sensing Mission Optimisation System (OSMOS) provides NOC with a mission rehearsal system which permits a range of sensing mission to be planned, optimised, rehearsed and finally monitored in order to progressively migrate towards a truly autonomous management system. OSMOS provides an open architecture framework for mission optimisation through a Dempster Shafer algorithm framework capable of determining a preferred swarm posture based upon expected target behaviours, platform capabilities and the research data outcomes. The design aim of the project is that by the completion of Phase 2 of the contract, the system shall provide a night-watchman capability to monitor a sensing programme for a 12 hour period and that the final evolution would provide for day to day control of a sensing programme, freeing the NOC science community to operate an oversight and directing role.

4 SBRI NOC-AAOSN-208 Autonomous Network of Teleological Sampling (ANTS) Marine South East Simple-minded robot vessels are increasingly being used to gather information on the oceans. The measurements they make add to our knowledge of how the seas work. For example, data gathered by these robots on horizontal and vertical currents help scientists to understand the distribution and growth of plankton, the food for fish and marine mammals. Such robots have also helped industry track the fate of oil spills undersea. But, today, it is more accurate to describe these robots as being rather dumb rather than being able to make their own decisions about how best to gather data. Of course they follow tracks or headings given to them by the pilot on shore, some can avoid collisions with other vessels when on the surface, those that operate underwater can avoid crashing into the seabed. They already have those instinctive reactions. Industry and scientists need their sea-going robots to work in cleverer ways. Our project will give these robots the ability to do more than follow simple tracks irrespective of the information contained in the measurements they are making. For now, much of the creative thinking will be done on-shore, and then communicated to the robots at sea. We do have the equivalent of weather forecasts for the ocean. What we will do is to combine the measurements from the robots with our best-guess of how the ocean will change, and use that knowledge to plan where best to send the robots, and what measurements to make. Take the example of an underwater plume of oil being wafted along by a current. No longer will the robots be blindly following a track decided upon by a pilot before they had any knowledge of the plume. Rather, the measurements made by the robot, as it finds the plume, will be sent ashore, and in an automated system, with a simulation model that predicts "where next", commands will automatically be sent to the robot to focus its effort on the most likely patch of sea. We've provided one example of what will develop into a set of tools to give sea-going robots a wideranging ability to track marine mammals, find new hydrothermal vent sites on the sea-floor, track fish as they feed and migrate, and map complex features such as canyons and reefs on the seabed. Our clients will use these tools to make better use of their investments in robots, and provide better information on the problems they face.

5 SBRI NOC-AAOSN-209 AOSN Mission Definition and Update System (AMDUS) Frazer Nash Consultancy AOSN Mission Definition and Update System (AMDUS) is an automatic Command and Control tool that will direct a formation of Marine Autonomous Systems (MAS) to gather the maximum data around a marine feature, while extracting the maximum utility of each component MAS. AMDUS will offload a significant workload from the NOC operator by showing the operator, in a graphical interface, the best options for a selected scenario based on a statistical model of feature movement, optimal formation, MAS performance and battery life, and sensor capabilities. The parameters outlined will feed into a bespoke marine simulation environment which will evaluate the most likely scenario to yield quality data and command each MAS accordingly. Ongoing missions can be tracked against the chosen plan and, if necessary, altered to ensure the maximum quality of data is gathered. With increased data quality, front movement will be characterised more accurately. The software will be able to run autonomously for at least 15 days with a primary science objective. An automatic interrupt may be configured to alter the primary objective in the event of a transient occurrence with a higher importance of data. This may be an event such as a new front, a chance encounter with a cetacean or a leak detected. The software will automatically reconfigure all or part of the fleet for maximum data gathering, and return to the primary objective if the transient phenomenon is no longer detected. The solution to the call will draw on Frazer-Nash s expertise in the space sector and marine sectors. The key technical approach will be to use and adapt the Potential Functions used in the navigation and control of the Automatic Transfer Vehicle (ATV) used by the European Space Agency to the underwater domain. Using Potential Functions for path optimisation will allow optimal path following, while maintaining formation, to achieve all 5 of the challenges posed: to follow a cetacean or fish, to follow a tidal front or identify the source of a leak, and to map the seabed. This method extends to the entire surface fleet, and includes underwater assets, enabling maximum data collection. It may also be expanded to assets above the water, for example UAVs.