Diver interest via pointing in three dimensions: 3D pointing reconstruction for diver-AUV communication

It is said generally that the history of autonomous underwater vehicles (AUVs) started with SPURV which was developed by APL of the University of Washington at the request of US Navy in 1957. This was an untethered underwater vehicle with a total length of 3.1 m and a displacement of 430 kg driven by a mono-axis motor and a silver-zinc battery. It was able to run with a maximum depth of 3600 m, a speed of 2–2.5 kt and a maximum cruising endurance of 5.5 h. For control, 12 bit preset command was used to change the steps of azimuth and depth, and hardwired logic circuit was used in the controller. The azimuth was controlled by the offset from the initial azimuth at launch. The roll is secured by the static stability of the body. The temperature and conductivity sensors were installed on the nose section, and the data was recorded on a tape recorder. This vehicle was used for wide-area ocean observation by US Navy.1 A modern AUV, for example, the REMUS 600 developed by Woods Hole Oceanographic Institution having the same size scale as SPURV, has an inertial navigation system of ring laser gyro fully integrated with a Doppler velocity log (DVL) and acoustic positioning system. Autopilot software enables independent control of fins providing yaw, pitch and roll control, altitude, depth, and track-line following. Furthermore, it can be equipped with optional forward fins available to maintain a straight heading in a cross current. This alignment and stability is essential for optimizing the performance of a synthetic aperture sonar. Various kind of sensors like synthetic aperture sonar (SAS), side scan sonar (SSS), and multibeam echo sounder (MBES) can be equipped as the payload sensor. This can run for up to 24 h with lithium-ion rechargeable batteries and it is also possible to avoid obstacles by using a front sonar.2 In a half century, due to the progress of digital technologies, sensor technology, and battery technologies, AUVs have made significant progress and they have been used widely for oceanography, industries, and defense. One of the most representative examples of AUV's strengths in wide-area subsea search was for Air France Flight 447 which crashed off the coast of Brazil in 2009. Multiple REMUS 6000, which is equipped with SSS and cameras, were used in this search activity, and after about 2 months of operation, they finally discovered the wreckage of an aircraft lying about 4000 m deep seabed and contributed to the recovery of the flight and voice recorders.3 From the perspective of industry, the AUV industry has moved from prototype development for research and development to mass production of systems now. However, the number of AUV manufactures is still limited, for example, “Husin” of Kongsberg in Norway,4 “Remus series” of Hydroid in United States (now a subsidiary of Huntington Ingalls Industries), and “Bluefin series” of Bluefin Robotics in United States (a subsidiary of General Dynamics Mission Systems).5 Mitsubishi Heavy Industries, Ltd. (MHI) had developed R&D AUV such as Urashima6 for JAMSTEC, and currently is manufacturing OZZ -5 AUV production model for the Ministry of Defense in Japan.7 This article mainly describes the expected future operation of AUVs to motivate AUV research and development in the academic world. This section describes the typical operation of an AUV, which is a prerequisite for a concrete view of the control system. As its name implies, AUV is an autonomous underwater vehicle that does not have a tether cable for power supply and communication. After it is launched from the surface vessel, it basically navigates autonomously according to a predetermined route plan because its underwater communication capacity is very limited. The first AUV, SPURV, traveled through the “empty” ocean to measure sea water temperature and conductivity. Therefore, it did not have sensors to detect the surrounding terrain. On the other hand, the main mission of the modern AUV is to survey the seafloor structure, or objects such as unexploded ordnance (UXO) using an underwater acoustic sonar. Since the AUV navigates at a relatively low altitude, it navigates autonomously while recognizing the seafloor and obstacles using DVL. This can detect the vertical altitude, and the forward looking sonar, which can detect obstacles and the seafloor ahead. In addition, in order to cover the search area with on-board sensors, a typical mission pattern is to cruise at a certain depth or altitude in a certain pattern (typically a lawnmower pattern), and maintain a constant speed so that the sensor detection range covers the search area. This mission pattern is planned and preset as a route plan before launch. In order to acquire data of the sonar, especially in the case of the synthetic aperture sonar, it is necessary to suppress the attitude fluctuations and keep straight for a crossing tidal current using a forward rudder as well. The AUV basically runs along a preset route plan unless there is an obstacle or emergency. After the crushing of the mission pattern is completed, AUV returns to the planned position and is recovered by the surface vessel. The acquired data is transferred to the computer on the vessel, and maintenance work such as battery charging is conducted, and then, the operation is repeated. Since the amount of the underwater acoustic sonar data is enormous and the transmission by underwater communication is limited, the data is evaluated by the human for the first time after the data analysis on the vessel. The next route plan may then be corrected depending on the data acquisition results. It should be noted that launch and recovery, especially recovery, is troublesome and dangerous work at sea due to the lack of a tether cable for the AUV, and the limitations under rough sea conditions. This leads to mission standby on the vessel and a resulting cost. After the AUV undersea survey, ROV may be used for detailed observation and underwater work depending on the purpose of operation. This is because the resolution of acoustic sonar is not as fine as the visual method, although it is possible to detect an “object like the target.” So the ROV needs to identify and determine “the target” using a visual or optical method, which has finer resolution than acoustic. Conventional AUV operation such as seabed surveys, or unknown object is almost the same procedure as described. Unlike such conventional operations, innovative technological developments for more efficient operations have been carried out for some applications. One such development is AUV's underwater docking technology, which has already been tried in various research studies. This allows the AUV to dock with an underwater docking station, charging electrical power to the AUV and transmitting data from the AUV, continuously to operate without launch and recovery. This has the possibility of providing innovative efficient operation of the AUV. To realize underwater docking, there are technical problems of a proximity sensor, a docking device, and guidance and attitude control. In some docking systems, mono-axial propulsion AUV, which is difficult to cruse at low speed, is caught by pushing it into a docking cage. However, an AUV has protrusions such as Fin, GPS/Wi-Fi antennas, and acoustic communication devices and also has a rubber surface for acoustic sensors. So this approach is risky for the AUV itself. Therefore, in consideration of the future expansion of AUV operations to be described later, it would be more feasible to adopt an approach in which an AUV capable of low speed or hovering is guided to precise docking positions and attitude, stabilized, and safely captured. Furthermore, the docking technology also can be applied to automatic launch and recovery operations. In order to improve the search efficiency, it is easy to come up with multiple AUV operations. In fact, several AUV's were used in the aforementioned Air France 447 search. Although it is only necessary to allocate each search area, it is necessary to introduce the viewpoint of automatic optimizing route planning. This is in order to eliminate duplication and omission of search areas, to consider work procedures such as launch and recovery, to adapt and easily change the plan according to the search situation, and to improve the overall efficiency. Furthermore, it will be possible that the operation of multiple vehicles will reach the level of Swarm operation by the ultra-small AUV. Airborne drones have been demonstrated to control 1000 groups, however they are still nothing more than technical interesting in terms of practical operations. Innovative practical ideas are required. In addition to multiple AUV operation, it is necessary to automatically optimize by combining heterogeneous unmanned vehicles in consideration of the entire practical operation, such as by cooperating with an autonomous surface vehicle (ASV) and the ROV. Even in the conventional operation pattern, there are technical problems to improve the search efficiency of AUV, and some of them are introduced below. At present, the AUV only controls the operation of its mission sensors such as SSS according to the pre-determined route plan. However, it is also conceivable that the search will be more effective by changing the route plan autonomously in real time according to the state of data acquisition by the mission sensor. For example, it is conceivable that, as a result of the automatic detection of a target like object in real time by a wide-area search sensor such as SSS, the wide-area search can be temporarily suspended, and then circling around the target area to obtain multi-angle data. It is also conceivable closing to the target to obtain the detailed data by using a fine resolution sensor such as underwater camera. This type of advanced operation eliminates the need for a second time operation, such as the “reacquisition” operation by ROVs, after the operation of the AUV, and will greatly improve the overall operation. Of course, some operators want to avoid the AUV having unintended behavior, so it is necessary to consider that the operator makes the final decision by using underwater communications. Such advanced autonomous behavior requires not only real-time automatic recognition technology of the sensor technology, but also the AUV itself is capable of low-speed cruising or hovering for closing approach to the target object. Both local route planning for the closing approach and route replanning to restart wide area search are necessary in real time and optimally. Despite the innovative autonomy in the previous section, there is a need to cruise at a lower altitude at a lower speed that is difficult for control. This is in order to obtain more detailed acoustic data or to survey with a short-range high-resolution sensor such as an underwater camera or laser. The lower the altitude, the more meticulous the route planning, and the more precise tracking and attitude control, considering sensor conditions, are required for complex terrains. : Vehicle velocity (velocity and angular velocity for each three axes). : Mass and added mass property terms. : Coriolis and centripetal terms. : Hydrodynamic damping force terms. : Hydrostatic force terms related to the Euler angle : Generalized actuator force and moment terms. There have been problems with the AUV control, such as disturbances caused by tidal currents, uncertainty of the model characteristics (e.g., fluid dynamics coefficients) and sensor data, nonlinearity of the actuators. The control engineers have designed and implemented an acceptable control system against such problems by conventional PID methods, linearizing the equation of motion and separating the control plane: vertical (pitch and depth) and horizontal (yaw and roll). However, PID control will be no longer applicable, when the AUV mission requires various maneuvers over a wide range of speeds including hovering, and side thrusters have been added to the AUV. AUV control engineers are facing the problems, such as, onboard reroute-planning, precise nonlinear maneuvering control in a wide range of velocities, while satisfying optimality and constraints such as energy limitations, attitude limitations for acquiring sensor data and actuator limitations in addition to the conventional problems of disturbances and uncertainties in the model. As for the development process, model-based development has already become main stream of control, and it is not necessary to discuss the merits of model-based developments here. In addition to this, it is expected that the development environment, simulation environment, and implementation environment will be seamlessly linked for an efficient development environment. In addition, considering the verification in three dimensions, such as in the complicated terrain tracking, it is difficult to evaluate only by the conventional time history graph. Visualization tools such as three-dimensional animation are required. Furthermore, considering the direct sharing of development methods and results, and the indirect sharing through education and human resource exchange, standardization of development, evaluation, and implementation environments is strongly required. De facto standards have already emerged. In this way, the efficiency of the development process on desk work has been improved, however when it comes to field testing, it remains unchanged. Running a system for the first time in water without tether cables and relying solely on acoustic communication, is still a major risk. After the initial test is completed and the vehicle starts to run, it does not follow the simulation because the AUV's body characteristics, especially the hydrodynamic characteristics, have the uncertainties mentioned. After correcting the control gain by trial-and-error, it should finally run as intended. Previously, the evaluation items were less due to its simple mission. However, in future, when AUV missions will be complex and evaluation items will become larger, we will not able to do such trial-and-error testing by human engineers. It leads to a huge number of sea trials and costs can be enormous. To avoid these high cost situations, adaptive methods are desirable. Adaptive control methods have been researched in the past, and recently, AI or re-enforcement learning is a rapidly growing area. We expect it will contribute in minimizing the development cost and operational cost. In the field of aerial robotics, the spread of low cost GPS, MEMS gyros, and microcomputers, as well as the development of software and network technologies, has led to the remarkable success of quadcopters. Drone has becomes the synonym of quadcopter although it means all kinds of unmanned robot. Drone is rapidly expanding their application from industrial applications such as surveying, agriculture, and entertainment to individual hobbies. Land robots are also a huge industry in terms of autonomous driving. On the other hand, underwater robots require special know-how such as pressure resistant structures and watertight structures in an underwater environment, and they are difficult to be recover when water leaks and sinks due to a small mistake. In addition, much of the equipment on board is expensive, such as underwater acoustic devices. For this reason, only a few manufacturers and research institutes with many years of experience in underwater vehicles can produce them, and their use is still limited to some marine industries and to defense fields. However, when considering the maturity of the technology, it is important to expand the scope of the industry, and for that purpose, it is important to reduce the AUV cost itself and its operating costs. From the development process of AUV to the improvement of operational efficiency by the innovation and upgrading of the operation, and so forth, the role of the control system is huge. We conclude with the hope that research and development and education on AUV control systems will continue to develop. I would like to thank to Chief Editor, Prof. M. J. Grimble to give me a chance to contribute this article, and Prof. I. Yamamoto, to advise and review the contents. And I am grateful to my colleagues on discussion about AUV control for the future.

In this chapter, the receding horizon Kalman filter is applied to underwater navigation systems. The ocean covers about two-thirds of the earth and has a great effect on human beings. However, the ocean is overlooked while we focus our attention on land and atmospheric issues; we have not been able to explore the full depths of the ocean, its abundant livings and non-living resources. For example, only recently we have discovered, by using manned submersibles, that a large amount of methane and carbon dioxide comes from the seafloor and extraordinary groups of organisms live in hydrothermal vent areas. However, a number of complex issues due to the unstructured and hazardous undersea environment make it difficult to survey in the ocean even though today’s technologies have allowed humans to land on the moon and robots to travel to Mars. Unmanned underwater vehicles (UUVs) can help us better understand marine and other environmental issues, protect the ocean resources of the earth from pollution, and efficiently utilize them for human welfare. The UUV is a platform for a variety of sensors: acoustic, magnetic, gravimetric and chemical ones. Most commercial UUVs are tethered and remotely operated, referred to as remotely operated vehicles (ROVs). Extensive use of manned submersibles and ROVs are currently limited to a few applications because of very high operational costs, operator fatigue and safety issues. The demand for advanced underwater vehicle technologies is growing and will eventually lead to fully autonomous and reliable underwater vehicles. Autonomous underwater vehicles (AUVs) were initially developed to perform missions that were not easy for ROVs and manned underwater vehicles. Since the autonomy allows AUVs to be used for risky missions such as a mine countermeasure (MCM) or under-ice operations, AUVs are replacing ROVs towed vehicles as well as manned underwater vehicles (Whitcomb, 2000). For detailed ocean surveys, an AUV acts as a more stable platform for precision sensors than ROVs or towed vehicles because an AUV is not subject to physical disturbances transmitted along the cable to the surface vessel. This absence of physical attachment also allows AUVs to measure ocean characteristics at specific depths and perform bottom-following missions as owing to its autonomy. In short, An AUV provides marine researchers with a new form of access to deeper ocean. For an AUV to successfully complete a typical survey mission, it must follow a path specified by the operator as closely as possible and arrive at a precise location for collecting data. When an AUV is not able to follow the path accurately during the mission, critical Source: Kalman Filter, Book edited by: Vedran Kordic, ISBN 978-953-307-094-0, pp. 390, May 2010, INTECH, Croatia, downloaded from SCIYO.COM

In the field of underwater robotics, Autonomous Underwater Vehicles (AUV) have made many advancements in operating depth, mission endurance, and acoustic range making them the ideal vehicle for surveying and searching for any Object of Interest (OOI) over large areas of water. The downside to this technology is that the operator must wait for the vehicle’s mission to end to determine whether an OOI has been identified. Additionally, if an OOI is identified this object will need to be found again. The solution to this lengthy process is to equip the AUV with a suite of Underwater Locator Beacons (ULB) which can be deployed and anchored next to any positively identified OOI. This way, the operator can be actively listening for the pinging frequency of a deployed ULB where then a secondary Remotely Operated Vehicle (ROV) can be launched to retrieve or further investigate the OOI while the AUV continues its search and tag. This paper presents the design and test of a ULB deployment system that would be implemented into an AUV. An AUV is sensitive to changes in weight, therefore this novel design leverages the concepts of Archimedes Principle by preserving neutral buoyancy pre- and post-deployment of the ULB. Upon deployment, the ULB will be capable of securely anchoring itself in a wide range of seabed environments. To test the design described above, a custom ROV has been fabricated with the sole purpose of transporting the ULB deployment system to operating depth. The paper describes in detail both the test results from the ULB deployment system and a design for implementation into an AUV.

Autonomous marine systems, such as autonomous ships and autonomous underwater vehicles, gain increased interest in industry and academia. Expected benefits of autonomous marine system in comparison to conventional marine systems are reduced cost, reduced risk to operators, and increased efficiency of such systems. Autonomous underwater vehicles are applied in scientific, commercial, and military applications for surveys and inspections of the sea floor, the water column, marine structures, and objects of interest. Autonomous underwater vehicles are costly vehicles and may carry expensive payloads. Hence, risk models are needed to assess the mission success before a mission and adapt the mission plan if necessary. The operators prepare and interact with autonomous underwater vehicles to carry out a mission successfully. Risk models need to reflect these interactions. This article presents a Bayesian belief network to assess the human–autonomy collaboration performance, as part of a risk model for autonomous underwater vehicle operation. Human–autonomy collaboration represents the joint performance of the human operators in conjunction with an autonomous system to achieve a mission aim. A case study shows that the human–autonomy collaboration can be improved in two ways: (1) through better training and inclusion of experienced operators and (2) through improved reliability of autonomous functions and situation awareness of vehicles. It is believed that the human–autonomy collaboration Bayesian belief network can improve autonomous underwater vehicle design and autonomous underwater vehicle operations by clarifying relationships between technical, human, and organizational factors and their influence on mission risk. The article focuses on autonomous underwater vehicle, but the results should be applicable to other types of autonomous marine systems.

This paper proposes a navigation method of multiple autonomous underwater vehicles (AUVs) with velocity control based on a single high-performance AUV which can estimate self-state (position and pose) accurately without any support. To understand the environment of the seafloor, photo and acoustic mapping by AUVs have been conducted. Since the AUVs cannot obtain GPS data directly in underwater environment, it is difficult to estimate self-state. In the proposed method, AUVs consist of two types of AUVs: a parent AUV and child AUVs where the former has high-grade navigational sensors and acts as a positioning reference; the latter has only low-cost navigational sensors. The child AUVs realize high positioning accuracy thanks to acoustic positioning relative to the parent AUV. However, positioning accuracy depends on distance between the parent and child AUVs. There is a risk that the positioning accuracy may degrade due to the child AUV moves away from the parent AUV. For that reason, the parent AUV transmits its own position and destination to the child AUVs, and each child AUV accelerates or decelerates its ground velocity based on its position and above information received from the parent AUV. As a result, each child AUV can realize seafloor observation while maintain its position relative to the parent AUV. Thanks to the proposed method, child AUVs can realize stable and accurate positioning relative to the parent AUV.

This paper proposes a method for underwater object speed estimation based on optical flow. How the dynamic properties of the interested target objects can be used to navigate autonomous underwater vehicles (AUVs) is studied. The interested objects are extracted by using the template matching method, and their dynamical properties are calculated using optical flow techniques. Using past and present information, the consecutive dynamic behavior of the object is estimated. The paper presents a sensor fusion scheme to derive the navigational commands for the AUV. The results on the extraction of the interested target in the image, calculation of optical flow, and estimation of the object speed are presented with experimental results. These results demonstrate that the performance of the proposed target tracking algorithm is a useful tool for underwater robots.

System integration and validation of embedded technologies has always been a challenge, particularly in the case of autonomous underwater vehicles (AUVs). The inaccessibility of the remote environment combined with the cost of field operations have been the main obstacles to the maturity and evolution of underwater technologies. Additionally, the analysis of embedded technologies is hampered by data processing and analysis time lags, due to low bandwidth data communications with the underwater platform. This makes realworld monitoring and testing challenging for the developer/operator as they are unable to react quickly or in real-time to the remote platform stimuli. This chapter discusses the different testing techniques useful for unmanned underwater vehicle (UUVs) and gives example applications where necessary. Later sections digress into more detail about a new novel framework called the Augmented Reality Framework (ARF) and its applications on improving pre-real-world testing facilities for UUVs. To begin with more background is given on current testing techniques and their uses. To begin with some background is given about Autonomous Underwater Vehicles (AUVs). An AUV (Healey et al., 1995) is a type of UUV. The difference between AUVs and Remotely operated vehicles (ROVs) is that AUVs employ intelligence, such as sensing and automatic decision making, allowing them to perform tasks autonomously, whilst ROVs are controlled remotely by a human with communications running down a tether. AUVs can operate for long periods of time without communication with an operator as they run a predefined mission plan. An operator can design missions for multiple AUVs and monitor their progress in parallel. ROVs require at least one pilot per ROV controlling them continuously. The cost of using AUVs should be drastically reduced compared with ROVs providing the AUV technology is mature enough to execute the task as well as an ROV. AUVs have no tether, or physical connection with surface vessels, and therefore are free to move without restriction around or inside complex structures. AUVs can be smaller and have lower powered thrusters than ROVs because they do not have to drag a tether behind them. Tethers can be thousands of metres in length for deep sea missions and consequently very heavy. In general, AUVs require less infrastructure than ROVs i.e. ROVs usually require a large ship and crew to operate which is not required with an AUV due to being easier to deploy and recover. In general, autonomous vehicles (Zyda et al., 1990) can go where humans cannot, do not want to, or in more relaxed terms they are suited to doing the “the dull, the dirty, and the O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

The exploration of oceans using autonomous underwater vehicles (AUVs) is necessary for activities, such as the sustainable management of fishery resources, extraction of seafloor minerals and energy resources, and inspection of underwater infrastructure. As the next step in ocean exploration, AUVs are expected to employ end-effectors to make physical contact with seafloor creatures and materials. We propose a scenario for realizing a sampling mission using an AUV that is equipped to sample marine life. In this scenario, the sampling AUV observes the seafloor while concurrently transmitting the observed images to a surface vessel for inspection by the AUV operators. If the received images show an object of interest, the object is selected as a candidate of sampling target by the operators, who send a sampling command to the AUV. After receiving the command, the AUV returns to the target area and attempts to sample it. In this paper, we propose a system for transmitting images of the seafloor as part of the sampling-mission scenario. The proposed image transmission system includes a process for enhancing images of the deep seafloor, a process for selecting interesting images, and processes for compressing and reconstructing images. The image enhancement process resolves imaging problems resulting from light attenuation, such as color attenuation and uneven illumination. The process for selecting interesting images selects those that contain interesting objects, such as marine life. The selection process prevents the transmission of meaningless images that contain only flat sand on the seafloor. The proposed image compression method, which is based on color depth compression, reduces the amount of data. The combined process of selecting an interesting image and compressing it reduces various problems in acoustic communication, such as low information density and data loss. Instead of an overall image, part of an overall image is transmitted by a set of data packet, and each received data packet is reconstructed onboard the vessel. Because of image compression, the colors of a reconstructed image differ from those of an enhanced image. However, the reconstructed image contains similar colors, and the structural similarity index was found to be 91.4% by evaluating images that were subjected to a 4-b color compression. The proposed image transmission system was tested in the Sea of Okhotsk, and these tests were performed four times in different sea areas (minimum depth 380 m, maximum depth 590 m). The results show that the size of the data for a single image was reduced by a factor of 18 using the proposed image compression process, with each image taking 3.7 s to be transmitted via an acoustic modem (20 kb/s). Of the automatically selected images, 63% contained marine life, and the total transmission success rate was 22%.

In January 2001, C & C Technologies, Inc. placed into full operation the first commercial deep-water Autonomous Underwater Vehicle (AUV) in the Gulf of Mexico. The application of this advanced technology was immediately beneficial to the survey industry as well as having a profound impact on deep-water archaeology. Over twenty-four shipwrecks and several additional potential wrecks have been discovered and/or investigated using C & C's AUVs. Several of those surveys resulted in new discoveries and at least, thirteen of the wrecks have been confirmed to be historic vessels. The initial primary geophysical instrument payload of the HUGIN model AUV consisted of dual frequency side scan sonar, subbottom profiler, and multibeam bathymetry. In 2009, C & C added a digital still camera to the vehicle allowing relatively quick visual confirmation of seafloor targets. The photos and photo mosaics produced from the camera surveys have further advanced deep-water archaeological investigation and mapping. This paper focuses on the archaeological application of the AUVs and in particular the photographic capabilities and utilization of those photographs. Introduction Autonomous Underwater Vehicles (AUVs) are built for a variety of purposes and come in many shapes and sizes with near limitless combinations of sensors and payloads. Camera systems are a relatively new addition to some of the deeper AUV systems. Currently, there are only a few companies, institutions, or government agencies (e.g. C & C Technologies Inc. (C & C), Kongsberg, Woods Hole Oceanographic Institution (WHOI), the United States Navy, etc.) that operate AUVs equipped with digital still cameras capable of survey to 1,000 meters or deeper. This paper will focus primarily on the C-Surveyor AUVs, which are HUGIN 1,000, 3,000, and 4,500 meter systems (Figure 1). All references to " AUV?? in this paper refer to one of the C-Surveyor AUVs unless otherwise specifically stated. Four AUVs have been added to C & C's fleet over the last decade as well as several payload and sensor upgrades. Although the sensor payload of each AUV may be slightly different, the basic payloads include an EM 2000 multibeam bathymetry system (Figure 2), Chip Edgtech subbottom profiler system (Figure 3), and duel frequency side scan sonar at 410 kHz and either a standard 120 kHz frequency or a 230 kHz dynamically focused sonar system (Figure 4). Three of C & C's AUVs (the 3,000 and 4,500 meter systems) are equipped with digital still cameras (George 2009a). The navigation/positioning system for the AUVs utilize a Kalman filter algorithm, which uses input data from a Simrad High Precision Acoustic Positioning (HiPAP) System, inertial navigation, and Doppler velocity speed log. The post-processed positions for the AUV are accurate to within ± 5 meters at a depth of 3,000 meters.

An approach is proposed for substantiation of the requirements for the use of the means of control of a technical state and diagnostics of failures of the electro-pneumatic steering gear with a hydraulic brake for the autonomous underwater vehicles. The extreme requirements are based on the use and means of control of a technical state and diagnostics of failures of the electro-pneumatic steering gear with a hydraulic brake for the autonomous underwater vehicles. Using the methods of the system analysis, a decomposition of the extreme requirements up to the level of the control functions and diagnostics of the steering gear was carried out. The methods and algorithms for control and diagnostics of the steering gear, a set of technological control and test equipment were developed. The novelty of the developed methods and algorithms for the control and diagnostics of the steering gear of the autonomous underwater vehicles is related to the formalization of the process of justification of the requirements to the use of the autonomous underwater vehicles, with transition from the requirements to the functions of the devices and, further, to the methods and algorithms of control and diagnostics of the steering gear. This allows us to reduce the probability of making wrong decisions at the stage of development of the equipment, which are the most frequent and lead to a significant increase of the cost and time of the research and development, and also create the technological equipment, methods and algorithms for control of the technical state and diagnostics of failures of a new type of the hybrid actuators - the elec-tropneumatic steering gear with a hydraulic brake. For testing of the proposed approach an autonomous unmanned underwater vehicle, which moves in water at a depth up to 30 m with velocities over 100 km per hour, was considered. The control of the autonomous underwater vehicle is performed by two pairs of rudders, which, depending on the driving mode of the device, take position from 0 to 90 degrees relative to the vehicle hull.

National Institute of Ocean Technology (NIOT), under the Ministry of Earth Sciences, along with IEEE OES and OSIs, conducts a national-level competition for students pursuing engineering degree to visualize and design an autonomous underwater vehicle. The conceptual basis for Student Autonomous underwater Vehicle (SAVe) is a highly mobile autonomous underwater vehicle (AUV) to be built based on engineering principles. This innovative initiative was launched in 2011 and so far NIOT had received 17,473 website hits, 257 registrations were made and 127 teams had submitted their Preliminary Design Reports (PDR) and 60 teams made oral presentation of Conceptual Design Reports (CDR) to improve their presentation and handle question and answer skills; 28 teams participated in the final competition and demonstrated their working and engineered AUVs at swimming pool. Most of the teams used 4–5 thruster configurations to have 6 DOF controlled by mostly Inertial Measurement Unit (IMU) interfaced with control unit (CPU) and powered by commercial LiPo battery packs. Till now, 3 teams had participated in International competition held at AUVSI foundation San Diego, USA and totally 8 prototypes of AUVs were developed by engineering students in India since year 2011. The aim of this competition was to involve young engineering students on the new frontiers of ocean technology and kindle their innovative thinking in this unexplored area of ocean environment and observation. The most common configuration of the student AUVs is that the linear dimensions of the AUVs are less than 1.5 m in length and weight is less than 35 kgs. The AUV design is a modular hydrodynamic hull structure and made up of acrylic material; mounted on Aluminium metallic frames. Many teams came up with modular thruster mounting frames which could help position the thrusters for good attitude control and this proved good stability of the vehicle against unwanted roll and pitch. All the teams were suggested to use maximum of 4 number of thrusters (for 6 degrees of freedom) to optimize the AUVs operation for considerable maneuverability with good energy efficiency and high endurance. Almost all the student AUVs get power supply from Lithium-Polymer (Li-Po) batteries with either 18.5 V or 11.1 V DC input to provide supply for the 19.1 V DC Thrusters and 12 V Mother Board. One of the most common features of the teams was Arduino microcontroller for controlling the thrusters interfaced with CPU. CPU configurations and capabilities of the teams processor speed varied from 1.6GHz to 2.1GHzsupported by 1GB or 2GB RAM. In fact, almost all the teams learned to use good quality web cameras for the underwater vision and image processing by placing them in sealed chambers. All the AUVs used face O-rings for the hulls for good sealing effect as well as for faster assembly and disassembly. Water resistant connectors were used to connect the AUV to supportive systems. The competition received overwhelming response from different institutions for which IEEE has come forward to extend financial support. The Office of Naval Research (ONR) also has shown interest to provide support for the competition to improve the awareness as well as encourage students in the field of underwater technologies.

Satellites and other Earth observation systems (EOSs) can image large portions of the ocean surface at one time. However, they cannot ascertain much about the underlying structure, nor can they perform in situ analysis. Autonomous underwater vehicles (AUVs) have the capability to perform such analyses and to explore the full structure of the water column, but their speed and energy limitations suggest that careful mission planning on the part of the operators is needed to effectively utilize their capabilities. By combining these two platforms, the amount of useful information gathered by the AUV is greatly increased, thereby increasing the total value of the combined EOS/AUV dataset. This paper discusses the relative strengths and weaknesses of the two systems and methods of combining them. The authors show in simulation that an AUV directed by an a priori dataset can gather more detailed information about an object of interest per unit of time than one which is not so directed.

With the development of the activities in deep sea, the application of the autonomous underwater vehicle (AUV) is very widespread and there is a prominent prospect. The development of an AUV includes many areas, such as vehicle (carrier/platform) design, architecture, motion control, intelligent planning and decision making, etc (Blidberg 1991; Xu et al., 2006). The researchers dedicate themselves to improving the performance of modular, low-cost AUVs in such applications as long-range oceanographic survey, autonomous docking, and shallow-water mine countermeasures. These goals can be achieved through the improvement of maneuvering precision and motion control capability with energy constraints. For low energy consumption, low resistance, and excellent maneuverability, fins are usually utilized to modify the AUV hydrodynamic force. An AUV with fins can do gyratory motion by vertical fins and do diving and rising motion by horizontal fins. Therefore, the control system of the propeller-fin-drived AUV is very different to the conventional only-propeller-drived AUV. A dynamic mathematic model for the AUV with fins based on a combination of theory and empirical data would provide an efficient platform for control system development, and an alternative to the typical trial-and-error method of control system tuning. Although some modeling and simulation methods have been proposed and applied (Conte et al., 1996; Timothy, 2001; Chang et al., 2002; Ridley, 2003; Li et al., 2005; Nahon, 2006; Silva et al., 2007), there is no standard procedure for modeling AUVs with fins in industry. Therefore, the simulation of the AUVs with fins is a challenge. This chapter describes the development and verification of a six Degree of Freedom (DOF), non-linear model for an AUV with fins. In the model, the external force and moment resulting from hydrostatics, hydrodynamic lift and drag, added mass, and the thrusters and fins are all analyzed and expressed in matrix form. The equations describing the rigid-body dynamics are left in non-linear form to better simulate the AUV inherently non-linear behavior. Motion simulation is achieved through numeric integration of the motion equations. The simulation output is then checked with the AUV dynamics data collected in experiments at sea. The comparison results show that the non-linear model gives an accurate estimation of the AUV’s acutal motion. The research objective of this project is the development of WEILONG mini-AUV, which is a small, low-cost platform serving in a range of oceanographic applications (Su et al., 2007). O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

Autonomous Underwater Vehicles For years, the oil and gas industry has used autonomous underwater vehicles (AUVs) for simple tasks such as underwater surveys and inspection. However, because of their technical limitations, AUVs have not been able to compete with remotely operated vehicles (ROVs), which are already standard equipment for most offshore projects. While the market for AUVs remains modest, they are being used in deepwater oil and gas plays around the world. “AUVs are not a commodity yet, but they are getting there,” said Richard Mills, AUV sales manager at Kongsberg Maritime. He added that one Kongsberg customer has a long-term AUV contract with Petrobras for the use of two vehicles offshore Brazil. High-resolution Large-area seabed surveys are the oil and gas industry’s primary use for AUVs today, said Mills. Although shiptowed sonar arrays have been used for decades in ocean floor mapping, the sensors cannot reach the absolute bottom in many cases. On the other hand, AUVs can glide just a few meters above the ocean floor to create higher-quality images of the seabed and subsurface. Despite some unique advantages, the power, communications, and launch-and-recovery systems of AUVs will need improvements in order to make the leap from rig jewelry to a trusted tool. Extended Missions To fully take advantage of an AUV’s tetherless capabilities, users must be able to operate the vehicle for longer periods of time before needing to bring it to the surface for recharging or recovery. Without advances in this area, the nagging question will be, “Why not use an ROV instead?” Liquid Robotics’ Wave Glider is an extreme example of AUV endurance. Able to harness energy from the movement of water and from a floating solar panel on the surface, Wave Gliders can travel for months without human intervention. They have even crossed oceans by themselves. But these vehicles travel very slowly and do not operate more than 100 ft below the surface. Propeller-driven AUVs, the main class used for oil and gas operations, have more power-intensive propulsion methods and tend to be much heavier than gliders. The most common power sources for light workclass vehicles, such as Kongsberg’s torpedo-shaped Hugin line, are rechargeable lithium ion batteries. Each year, new variations of lithium- based batteries are developed and tested, each with slightly better chemistry, but so far, they have offered only incremental improvements. Daniel Gomez-Ibanez, an engineer at Woods Hole Oceanographic Institution (WHOI), said that most propeller-driven AUVs have functional battery lives of 1 to 2 days, even though the total battery life of some are listed as long as 60 hours.

The HUGIN 1000 Arctic Class AUV system addresses new challenges of the Arctic environment. With its high Area Coverage Rate (ACR), made possible by use of interferometric synthetic aperture sonar (HISAS 1030) in combination with a multibeam echo sounder (EM 2040), survey time is reduced drastically, thereby reducing the risk and cost of the entire operation. Furthermore, HUGIN 1000 Arctic Class AUV features a number of enabling technologies for under-ice mapping, such as advanced collision avoidance algorithms specifically developed for under-ice operations, radio-through-ice localization and communication systems, and a DVL-aided INS based navigation system designed for use on the North Pole without numerical issues. The HUGIN Arctic Class AUV system is configured in two ISO containers, one 20-ft and one 10-ft, both insulated for Arctic climate, and shippable by land, air and sea. This highly portable AUV system can also be delivered with solutions for L&R through a moon pool or hole in the ice. Introduction The increase in human activity in the Arctic region has led to a need for improved knowledge about the subsea Arctic environment. In particular, requirements for bathymetric and geophysical mapping of the seafloor in ice-covered areas are increasing. These mapping requirements stem from several sources, including Minerals Management Service (MMS), academic research and national territory mapping. An autonomous underwater vehicle (AUV) is the natural tool to employ for under-ice surveying, in both shallow and deep waters. Traditionally, AUV-based surveys have provided significant cost benefits for deep-water work, where tow-body based systems suffer from comparatively lower operational efficiency. However, for efficient mapping of large areas under ice, AUVs are the only viable solution due to the presence of the ice itself. A range of new challenges arise when using AUVs for under-ice surveying. Apart from operating in a tougher and more remote environment, these include risk of collision with the ice, increased risk of a lost AUV, and a more challenging environment for maintaining long-range autonomous navigation accuracy. Additionally, AUV launch and recovery (L&R) operations, which represent the riskiest element of AUV operations even in open waters, will require innovative solutions to keep the risk of damage at an acceptable level. Arctic Applications for AUV Arctic AUV applications exist in several markets, and can be categorized into the following areas: Geophysical seabed mapping; marine research; offshore ice management; and environmental monitoring. Geophysical seabed mapping in the Arctic is particularly applicable to UNCLOS-related activities, where several countries have requirements to map the Arctic with the intent of defining their national territories. Also, oil and gas companies have the usual pre-exploration seabed mapping requirements, which are already being met with AUVs elsewhere in the world. For ice-infested areas, a specific shallow-water AUV application is to map gouges on the seafloor made by ice keels when large ice floes drift through an area of interest. Knowledge about these ice gouges, and how they change from year to year, gives oil companies valuable information about the risk associated with, and the requirements for, burying pipelines, amongst other activities.