Target defense differential game for autonomous surface vehicles

Capitalizing on the experience and technology developments gained with underwater autonomous vehicles, the current research frontier in the field of autonomous marine vehicles has moved from under water to the sea surface, i. e., autonomous sea surface vehicles. Current and future perspectives of these types of autonomous vehicles are given in Sect. 13.1, with particular attention paid to US navy current interests. These were initiated a decade ago and still actively drive developments in this sector. The ability to design craft specialized for particular tasks to reach the best performance at sea, going beyond size and operational/safety limitations currently imposed by manned ships, offers unique opportunities to the naval architect. In this respect, the basic naval architecture principles that drive the selection of a type of hull with respect to operational requirements are given in the Sect. 13.3. The selection of the type of hull is a preliminary essential activity for the successful design or acquisition of an autonomous surface craft. Finally, as a practical example, Sect. 13.4 summarizes the main results of extensive research done at the MIT-iShip lab to develop a new class of autonomous unmanned surface vehicles, based on a highly specialized and optimized design of an unconventional SWATH (small waterplane area twin hull) hull, able to achieve superior performance and operational capabilities in real sea state conditions.

During January and February, 2021, SeaSatellites Inc, (Seasats) in collaboration with the Scripps Ecological Observatory at Scripps Institution of Oceanography, conducted a series of sea trials to demonstrate the capability of collecting ocean water samples using a newly developed Programmable Water Sampling System (ProWaSS) that had been integrated into a solar/battery powered 3m (9.0ft) Seasats Autonomous Surface Vehicle. During the past decade there has been a steady growth in the number of autonomous surface vehicles being deployed to conduct a variety of missions ranging in duration of only a few hours to multiple days, weeks and in some cases multiple months. For many, the idea of deploying an autonomous surface vehicle for extended periods of time, in all- weather conditions while still performing the allotted tasks is a welcomed option. Alleviating the need to go to sea for long periods and now having seen the impact of the Covid-19 pandemic curtailing crewed ship activities, the autonomous surface and subsurface vehicle option has proven invaluable. Collecting water samples for microbial and eDNA analysis is key to better understanding the health of marine ecosystems. For example, knowing the location and density of organisms capable of producing a harmful algal bloom (HAB) is critical to predicting their landfall on beaches where they impact the health and safety of humans and marine wildlife with a potential for substantial financial loss due to closure of recreational and commercial enterprises on our coasts. One way to provide an early warning of HABs landing on coastal beaches is through regular offshore water sampling at HAB initiation sites. This can sometimes be challenging due to rough seas and the unavailability or expense of vessels. The Seasats autonomous surface vehicle equipped with a Programmable Water Sampling System (ProWaSS) allows sampling to commence when other types of sampling are difficult or impossible or crewed vessels are unavailable or operationally prohibited. Trials of the ProWaSS demonstrated the ability to repeatedly collect water samples at pre-determined GPS waypoints offshore of Scripps Pier, return to the pier where the Seasats vehicle was quickly and easily recovered and the samples sent to the laboratory for analysis. This paper and presentation describe the Seasats vehicle and the ProWaSS and presents the results of the water sample analysis provided by Dr Jeff Bowman and Elizabeth Connors from the Scripps Institution of Oceanography, La Jolla, CA, and proposed further development to expand the ProWaSS to accommodate additional water samples and the inclusion of data from CTD and fluorometer sensors.

The origins and effects of wave drag at and near the surface and in shallow water are discussed in terms of the dispersive waves generated by streamlined technical bodies of revolution and by semi-aquatic and aquatic animals with a view to bearing on issues regarding the design and function of autonomous surface and underwater vehicles. A simple two-dimensional model based on energy flux, allowing assessment of drag and its associated wave amplitude, is applied to surface swimming in Lesser Scaup ducks and is in good agreement with measured values. It is argued that hydrodynamic limitations to swimming at speeds associated with the critical Froude number (≈0.5) and hull speed do not necessarily set biological limitations as most behaviours occur well below the hull speed. From a comparative standpoint, the need for studies on the hull displacement of different forms is emphasized. For forms in surface proximity, drag is a function of both Froude and Reynolds numbers. Whilst the depth dependence of wave drag is not particularly sensitive to Reynolds number, its magnitude is, with smaller and slower forms subject to relatively less drag augmentation than larger, faster forms that generate additional resistance due to ventilation and spray. A quasi-steady approach to the hydrodynamics of swimming in shallow water identifies substantial drag increases relative to the deeply submerged case at Froude numbers of about 0.9 that could limit the performance of semi-aquatic and aquatic animals and autonomous vehicles. A comparative assessment of fast-starting trout and upside down catfish shows that the energy losses of fast-starting fish are likely to be less for fish in surface proximity in deep water than for those in shallow water. Further work on unsteady swimming in both circumstances is encouraged. Finally, perspectives are offered as to how autonomous surface and underwater vehicles in surface proximity and shallow water could function to avoid prohibitive hydrodynamic resistance, thereby increasing their operational life.

Plitvice Lakes National Park is the largest national park in Croatia and also the oldest from 1949. It was added to the UNESCO World Natural Heritage List in 1979, due to the unique physicochemical and biological conditions that have led to the creation of 16 named and several smaller unnamed lakes, which are cascading one into the next. Previous scientific research proved that the increased amount of dissolved organic matter (pollution) stops the travertine processes on Plitvice Lakes. Therefore, this complex, dynamic but also fragile geological, biological and hydrological system required a comprehensive limnological survey. Thirteen of the sixteen lakes mentioned above were initially surveyed from the air by an unmanned aircraft equipped with a survey grade GNSS and a full frame high-resolution full-screen camera. From these recordings, a georeferenced, high-resolution orthophoto was generated, on which the following surveys by a multibeam sonar depended. It is important to mention that this was the first time that these lakes had ever been surveyed both with the multibeam sonar technique and with such a high-resolution camera. Due to the fact that these thirteen lakes are difficult to reach and often too shallow for a boat-mounted sonar, a special autonomous surface vehicle was developed. The lakes were surveyed by the autonomous surface vehicle mounted with a multibeam sonar to create detailed bathymetric models of the lakes. The missions were planned for the surface vehicle based on the orthophoto from the preliminary studies. A detailed description of the methodology used to survey the different lakes is given here. In addition, the resulting high-resolution bathymetric maps are presented and analysed together with an overview of average, maximum depths and number of data points. Numerous interesting depressions, which are phenomena consistent with previous studies of Plitvice Lakes, are noted at the lake beds and their causes are discussed. This study shows the huge potential of remote sensing technologies integrated into autonomous vehicles in terms of much faster surveys, several orders of magnitude more data points (compared to manual surveys of a few decades ago), as well as data accuracy, precision and georeferencing.

Autonomous systems offer expanding capabilities to provide maritime services in a time where saving costs, creating efficiencies and improved safety are vital. Autonomous vehicles are complex systems and therefore their design and development requires careful and detailed planning. Autonomous Surface Vehicles (ASV) is a rapidly growing company based in the UK with offices in the United States and is a leading manufacturer of Unmanned and Autonomous Marine Systems. Utilising specialist expertise and experience in the design, build and operation of Marine Autonomous Systems, the C-Worker 6 Autonomous Surface Vehicle (ASV) was developed for marine operations support in the Oil & Gas industry. This paper explores the design and build process from a naval architecture, mechanical, electrical and software engineering point of view, from initial concepts to field operations. The authors also assess the presented method in a meta-design manner. With the initial concept and technology capabilities established, ASV collaborated with Oil and Gas service company Technip to establish industry requirements and define the final configurations accordingly through a dedicated technology qualification process. Technological advancements introduced in this 6 meter long vehicle known as the C-Worker 6, include the integration of multiple offshore payload combinations including USBL, ADCP (current meter), CTD, Multibeam Sonar, Acoustic Telemetry, and Passive Acoustic Sonar (PAM) for marine mammal detection. The robust design incorporating an aluminium, self-righting hull makes the vehicle suitable for harsh ocean environments. C-Worker 6 has a 30 day endurance at an average speed of 4 knots and houses fully redundant power propulsion and communication systems. As a result of the methodology applied, the product development timeline is presented. The paper also presents data evaluated from real missions. Early qualification of the vehicle has shown its ability to perform in the high sea states of the Gulf of Mexico successfully carrying out subsea positioning in 1300m deep waters with 2.5m waves, as well as having performed Touch Down Point (TDP) monitoring support for S-Lay pipe installation during the technology qualification. The vehicle has since undertaken different mission witch includes a 5 day deployment in the Irish Sea where it held station and extracted data from a subsea platform via an integrated acoustic modem payload, a multibeam survey on a future wind farm installation and Pacific Acoustic Monitoring (PAM) in the Gulf of Mexico. The interest for using unmanned and autonomous systems to support marine operations in Oil and Gas industry is anticipated to grow with the industry needs and requirements for more efficient, cost effective and safer solutions.

Autonomous exploration and rescue vehicles have been gaining wide interest over the past few years. Nowadays, demonstrations showed that those vehicles can fly, dive, surf, or drive while carrying out missions autonomously in some specific scenarios. Monitoring vehicles during missions is a crucial and challenging task to avoid the unnecessary cost of losing vehicles or potential accidents. In this paper, we present a cheap yet effective way for monitoring and communicating with autonomous vehicles over long distances by using off-the-shelf 900 MHz modems namely RFD 900+ and high gain antennas. Although the 900 MHz band has been around for over two decades, no complete analysis exists providing guidelines to use off the shelf modems for point-to-point and multi-point communications. Our main contribution is to provide experimental analysis of the communication capabilities in point-to-point and multi-point scenarios in both line of sight (LOS) and non line of sight (NLOS) using an affordable setup ($70 per modem). Experiments were carried out using autonomous surface vehicles (ASVs) as remote nodes and computers as Ground Control Stations (GCSs).

Within the next several years, there will be a high level of autonomous technology that will be available for widespread use, which will reduce labor costs, increase safety, save energy, enable difficult unmanned tasks in harsh environments, and eliminate human error. Compared to software development for other autonomous vehicles, maritime software development, especially in aging but still functional fleets, is described as being in a very early and emerging phase. This presents great challenges and opportunities for researchers and engineers to develop maritime autonomous systems. Recent progress in sensor and communication technology has introduced the use of autonomous surface vehicles (ASVs) in applications such as coastline surveillance, oceanographic observation, multi-vehicle cooperation, and search and rescue missions. Advanced artificial intelligence technology, especially deep learning (DL) methods that conduct nonlinear mapping with self-learning representations, has brought the concept of full autonomy one step closer to reality. This article reviews existing work on the implementation of DL methods in fields related to ASV. First, the scope of this work is described after reviewing surveys on ASV developments and technologies, which draws attention to the research gap between DL and maritime operations. Then, DL-based navigation, guidance, control (NGC) systems and cooperative operations are presented. Finally, this survey is completed by highlighting current challenges and future research directions.

Oil spill incidents in the sea or harbors occur with some regularity during exploration, production, and transport of petroleum products. In order to mitigate the impact of the oil spill in the marine life, immediate, safety, effective and eco-friendly actions must be taken. Autonomous vehicles can assume an important contribution by establishing a cooperative and coordinated intervention. This paper presents the development of a path planning control-law methods for an autonomous surface vehicle (ASV) being able to contour the oil spill while is deploying microorganisms and nutrients (bioremediation) capable of mitigating and contain the oil spill spread with the collaboration of a UAV vehicle. An oil spill simulation scenario was developed in Gazebo to support the evaluation of the cooperative actions between the ASV and UAV and to infer the ASV path planning for each one of the proposed control-law methods.

Effective ocean management requires integrated and sustainable ocean observing systems enabling us to map and understand ecosystem properties and the effects of human activities. Autonomous subsurface and surface vehicles, here collectively referred to as “gliders”, are part of such ocean observing systems providing high spatiotemporal resolution. In this paper, we present some of the results achieved through the project “Unmanned ocean vehicles, a flexible and cost-efficient offshore monitoring and data management approach—GLIDER”. In this project, three autonomous surface and underwater vehicles were deployed along the Lofoten–Vesterålen (LoVe) shelf-slope-oceanic system, in Arctic Norway. The aim of this effort was to test whether gliders equipped with novel sensors could effectively perform ecosystem surveys by recording physical, biogeochemical, and biological data simultaneously. From March to September 2018, a period of high biological activity in the area, the gliders were able to record a set of environmental parameters, including temperature, salinity, and oxygen, map the spatiotemporal distribution of zooplankton, and record cetacean vocalizations and anthropogenic noise. A subset of these parameters was effectively employed in near-real-time data assimilative ocean circulation models, improving their local predictive skills. The results presented here demonstrate that autonomous gliders can be effective long-term, remote, noninvasive ecosystem monitoring and research platforms capable of operating in high-latitude marine ecosystems. Accordingly, these platforms can record high-quality baseline environmental data in areas where extractive activities are planned and provide much-needed information for operational and management purposes.

Development of a solar-powered small autonomous surface vehicle for environmental measurements

... This article addresses the compatibility of autonomous vehicles (AV) with the principles of data protection law under the legal framework of the European Union/European Economic Area (EU/EEA). The importance of this article lies in the fact that most data protection law provisions stem from these principles. It is therefore impossible to speak of data protection law compliance without an initial compliance with these rudimentary principles. AV embody the most advanced stage of vehicle automation whereby the system is completely and independently responsible for all driving tasks everywhere and at all times without any form of human intervention.1 To function properly, AV rely heavily on big data,2 which includes both the personal and non-personal3 data of drivers, passengers, road users, pedestrians, and other relevant persons. This effectively means that for AV to function as desired, it must necessarily interact or engage with other AV, Internet of things (IOT),4 traffic signs, cloud services, and other connected devices. Considering the central role data plays in the mode of operations of AV, it is essential that personal data forms the basis of the discussions in this article. Where necessary, the nature of the data (whether personal or not) will be expressly mentioned and specified.5 In order to properly address the impact of the principles of data protection law on AV, this article examines some of the ways through which AV collect (personal) data and the principles of data protection law as they pertain to AV. The concluding parts of this article consider the relationship between the principles of data protection law and AV, the inherent data protection law compliance challenges as well as the recommendation of appropriate remediation actions that could help resolve identified challenges.

The overall aim of the ROSM project is the implementation of an innovative solution based on heterogeneous autonomous vehicles to tackle maritime pollution (in particular, oil spills). These solutions will be based on native microbial consortia with bioremediation capacity, and the adaptation of air and surface autonomous vehicles for in-situ release of autochthonous microorganisms (bioaugmentation) and nutrients (biostimulation). By doing so, these systems can be used as the first line of the responder to pollution incidents from several origins that may occur inside ports, around industrial and extraction facilities, or during transport activities, in a fast, efficient and low-cost way. The paper will address the development of a team of autonomous vehicles able to carry, as payload, native organisms to naturally degrade oil spills (avoiding the introduction of additional chemical or biological additives), the development of a multi-robot system able to provide a first line responses to oil spill incidents under unfavourable and harsh conditions with low human intervention, and then a decentralized cooperative planning with the ability to coordinate an efficient oil spill combat. Field tests have been performed in Leixoes Harbour in Porto and Medas, Portugal, with a simulated oil spill and validated the decentralized coordinated task between the autonomous surface vehicle (ASV) ROAZ and the unmanned aerial vehicle (UAV).

Monitoring and patrolling large water resources is a major challenge for nature conservation. The problem of acquiring data of an underlying environment that usually changes within time involves a proper formulation of the information. The use of Autonomous Surface Vehicles equipped with water quality sensor modules can serve as an early-warning system for contamination peak-detection, algae blooms monitoring, or oil-spill scenarios. In addition to information gathering, the vehicle must plan routes that are free of obstacles on non-convex static and dynamics maps. This work proposes a novel framework to obtain a collision-free policy using deterministic knowledge of the environment by means of a censoring operator and noisy networks that addresses the informative path planning with emphasis in temporal patrolling. Using information gain as a measure of the uncertainty reduction over data, it is proposed a Deep Q-Learning algorithm improved by a Q-Censoring mechanism for model-based obstacle avoidance. The obtained results demonstrate the effectiveness of the proposed algorithm for both cases in the Ypacaraí monitorization task. Simulations showed that the use of noisy-networks are a good choice for enhanced exploration, with 3 times less redundancy in the paths with respect to — greedy policy. Previous coverage strategies are also outperformed both in the accuracy of the obtained contamination model by a 13% on average and by a 37% in the detection of dangerous contamination peaks. Finally, the achieved results indicate the appropriateness of the proposed framework for monitoring scenarios with autonomous vehicles.

This paper presents the implementation of an innovative solution based on heterogeneous autonomous vehicles to tackle maritime pollution (in particular, oil spills). This solution is based on native microbial consortia with bioremediation capacity, and the adaptation of air and surface autonomous vehicles for in situ release of autochthonous microorganisms (bioaugmentation) and nutrients (biostimulation). By doing so, these systems can be applied as the first line of the response to pollution incidents from several origins that may occur inside ports, around industrial and extraction facilities, or in the open sea during transport activities in a fast, efficient, and low-cost way. The paper describes the work done in the development of a team of autonomous vehicles able to carry as payload, native organisms to naturally degrade oil spills (avoiding the introduction of additional chemical or biological additives), and the development of a multi-robot framework for efficient oil spill mitigation. Field tests have been performed in Portugal and Spain’s harbors, with a simulated oil spill, and the coordinate oil spill task between the autonomous surface vehicle (ASV) ROAZ and the unmanned aerial vehicle (UAV) STORK has been validated.

Hybrid Collision Avoidance for Autonomous Surface Vehicles