Autonomous underwater vehicles (AUVs) generate two streams of interest, one in the foreground (robotics and the maritime environment) and the other in the background (autonomy), as will be explained in the following two sections (1.1 and 1.2). In Section 1.3, the organization of the chapters, in line with the discussion regarding robotics, maritime environment and autonomy, is explained. Section 1.3 contains also abstracts of all chapters. Their summaries, conclusions and suggestions for future work are collected in Chapter 19, where we try a `localization and mapping' of the chapters' contents in the design space of AUVs, utilizing the coordinate system developed in this Introduction.

The goal of this chapter is to provide the reader with the appropriate analytical methods, in order to derive a simple yet accurate vehicle model, design a state estimation algorithm that could be easily integrated into the embedded system framework of an underwater robotic vehicle and perform a fast online dynamic parameter identification.

The first section of this chapter presents an NMPC strategy for underwater robotic vehicles operating under various constraints. The purpose of the controller is to guide the vehicle towards specific way -points. Various constraints such as obstacles, workspace boundaries and control input saturation as well as predefined upper bound of the vehicle velocity (requirements for several underwater tasks such as seabed inspection scenario and mosaicking) are considered during the control design. The proposed scheme incorporates the full dynamics of the vehicle in which the ocean currents are also involved. The controller is designed in order to find the optimal thrusts required for minimizing the way -point tracking error. Moreover, the controlinputs calculated by the proposed approach are formulated in a way that the vehicle will exploit the ocean currents, when they are in favor of the way -point tracking mission, which results in reduced energy consumption by the thrusters. In the second part of this chapter, novel position- and trajectory -tracking control schemes for AUVs are presented. The proposed controllers do not utilize the vehicle's dynamic model parameters and guarantee prescribed transient and steady-state performance despite the presence of external disturbances and kinematic constraints for the case of underactuated vehicles. Moreover, through the appropriate selection of certain performance functions, the proposed scheme can also guarantee the satisfaction of motion and performance constraints imposed by the desired task.

This chapter introduces a new class of ARC tracking control framework for AUV without any knowledge of the system dynamics parameters. Conventional ARC strategies either require complete/partial knowledge of the systems dynamics parameters or presume the overall uncertainty (or its time derivative) is upper bounded by a constant. However, such presumption is restrictive for systems like AUV which has explicit presence of states in its structure of system uncertainty.Besides, many of the existing AR-based designs suffer from the over-and underestimation problems of switching gains. In such regards, the proposed ASRC law not only avoids restrictive presumptions of system uncertainty being upper bounded by a constant, it simultaneously alleviates the over-and underestimation problems of switching gain. Futher, the effectiveness of the ASRC law is verified in simulation in comparison with the existing ASMC law under the scenario of uncertain system dynamics. An important future work would be to formulate an ARC for AUV considering input/output delay since data acquisition and localization in underwater scenario is always challenging and a controller needs to take care of such circumstances.

The term navigation is commonly used to describe several different but related concepts. Some use the term for the process of determining the position (in some reference frame) and the orientation of the vehicle only, whereas others also include the problem of how to get from a point A to a point B in the navigation concept. In this chapter, we focus solely on the former meaning of the term. In other words, we define navigation to mean estimation of the six degrees of freedom of a rigid body (any vehicle or device), i.e., the position and orientation of the body. The position can be expressed in various reference frames (local or global), and the orientation is defined as the spatial orientation of the body frame relative to a local reference tangential to the Earth ellipsoid. This is more precisely defined in Section 5.2. It is often customary to include the estimation of linear and angular velocities in the navigation problem. The uncertainties of the estimates are often also part of the navigation output, and the navigation can be performed in real time, or in post -processing.

This work develops a TAN algorithm that relies on basic motion sensors and bathymetric observations obtained by low-power sonars (e.g. a single-beam sounder or a downward-facing ADCP while in bottom -tracking regime) and is sufficiently robust to deal with low resolution bathymetric maps. The state estimation process is performed by utilising the Rao-Blackwellised particle filter (RBPF). To make the navigation filter computationally feasible while using low-power processing boards with limited computational resources, the filter estimates the 2D vehicle's position and the 2D speed of the local water currents. Therefore, the proposed navigation solution can enable AUV deployments in remote deep oceans of the order of months, rather than hours or days, without the need for external support or regular surfacing.

In the transiting stage of an unmanned undersea vehicle (UUV) mission, it is of interest to minimize platform localization error with minimal processing. Earlier work [1] derived a simultaneous localization and map building (SLAM) -inspired estimator of platform location and velocity, dubbed "velocity -over -ground" (VOG)-SLAM, that provides virtually identical performance in transit scenarios as conventional SLAM. The method lends itself to simple real-time operation as map building is not required. The "VOG" simplification was devised based on (a) the observation that the second measurement of a persistent contact was required for potential performance improvement in SLAM and (b) the intuitive idea that SLAM is providing velocity information since contact measurements can only be relative to the platform. We provide here a direct argument by arguing its optimality properties via connection to the maximum likelihood estimator (MLE). In addition, techniques for sonar data processing, measurement generation and data association methodologies to determine proper assignments between measurements and persistent bottom features are discussed. These further extend concepts found in [2]. The process can be currently completed before the next ping arrives suggesting near real-time SLAM performance in complex undersea environments

In recent years scientists perceived the advantage of unmanned vehicles in ocean exploration. They can extend the time of observation or reach areas that are difficult to access with conventional means. The market offers a diversity of options of unmanned vehicles, but mostly each system is self-contained, and it is difficult to operate in collaboration with others from different vendors. A fl exible tool that enables the interconnection and command of all assets independent of the vendor is a must if we want to integrate diverse assets from multiple vendors. LSTS toolchain for autonomous systems has been in the development for several years and is presented here.

In this chapter, we introduce the topic of telemetry for autonomous underwater vehicles (AUVs) and draw considerations on different techniques and approaches to be used when establishing underwater communications.

In underwater vehicles, the pressure vessels offer a particularly worthwhile starting point for reducing vehicle weight and vehicle size. Pressure vessels are needed because on -board batteries, engines, optics and, above all, electronic components like computers have been developed for operation in air and must be protected from corrosion and ambient pressure in the sea. Although the cavities in pressure vessels provide a certain amount of buoyancy, the thick-walled vessel walls made of glass, aluminium, titanium, stainless steel or other materials have to become thicker and thicker as the vehicles' operating depth increases, increasing their weight.

We describe an approach for accomplishing the high-level mission planning required for a heterogeneous team of autonomous vehicles performing surveys in multiple areas, such as required for mine countermeasure (MCM) missions. The high-level mission scheduling and waterspace management require sequencing the order and location of lower-level tasks to be completed by each vehicle in the heterogeneous team: unmanned surface vessels (USVs) and unmanned underwater vehicles (UUVs). In this context, the USVs serve as transport vehicles while the UUVs perform the actual surveys and execute any other local actions. We develop a solution to this complex sequencing operation by leveraging unique information processing, communication, refueling, and planning windows that form constraints within the system within a formal scheduling optimization framework. We use mixed-integer linear programming (MILP) as a solution method for the associated optimization problem. By using such a standard optimization approach, we both take advantage of optimality guarantees on the solution and extensive commercial numerical solvers. In addition to developing the numerical optimization problem, we also include methods to account for risks due to schedule slip for the individual tasks. We conclude with an example of joint USV/UUV planning using the optimization algorithm

This chapter presents the development of the small swarm -capable autonomous underwater vehicle (AUV) MONSUN and its use for environmental monitoring and inspection tasks. A summarizing description of robot development in hardware and software is given, whereby efforts of the design process are explained in detail. After the focus on underwater communication techniques, swarm behaviours based on robust and scaleable localization principles are presented. Finally, the flexibility and functionality of the system are shown with the help of various experiments and field tests.

This work details the combination of cooperative AUVs, accurate timing and clock synchronization, underwater sensors, and acoustic communication for the purposes of detection of various underwater targets for seismic surveys, surveillance, and marine mammals detection applications. Future work will include sensor fusion of passive sensor array of sensors of same kind or passive sensor array of sensors of different kind(magnetic and acoustic) similar to an underwater monitoring systems by introducing autonomous triggering based on acoustic presence indicators of marine species.

The robotics community is deeply interested in both platform design and behavior design, but we lack tools to connect the two. The platform, the behavior design, and the environment work together to determine the robot's actions, but our tools visualize the design of the hardware and the design of the behaviors separately. We lack tools that allow us to visualize the relationship between the platform and the behavior. To address this gap, we introduce a new design method based on a tabular representation called Capability Analysis Tables. The Capability Analysis Table enables the designer to define the constraints on a behavior design based on the platform it will be used on, and to define the constraints on the platform based on the behavior design. It gives the customer and the designer an opportunity to more clearly specify the desired behavior. Environmental factors are implicit in the platform interface definitions -sensory perception filters environmental inputs (colored objects can only be seen by sensors that produce color information) and actuators filter environmental outputs. Communications are handled explicitly as outputs and as either operator inputs or remotely gathered sensory data.

The objective of this chapter is to review fault tolerance in general for AUVs. This includes an update on an earlier review that addresses reactive AUV fault -tolerance strategies. The chapter concludes with motivation and suggestions on research areas that could have high impact.

The chapter describes the application of agile management methods in large hardware projects using the example of the development of autonomous underwater vehicles (AUVs). Our case study concerns the development, construction and testing of a fleet of five new AUVs with associated unmanned surface vehicles (USVs) over a period of less than 2 years. First, however, the most important terms of classical project management and agile methods are compared. The Agile Manifesto and agile management methods including SCRUM and Kanban, in comparison with SCRUM alone, as well as eXtreme programming (XP) from software development, are presented. The possibilities of agile methods in the development of physically based systems such as automobiles are shown by the example of eXtreme manufacturing (i.e. Wikispeed). The experiences of the project itself, along with its participation in the Shell Ocean Discovery )(PRIZE, are presented and discussed, deriving from it a development methodology.

This chapter describes the main challenges, and the corresponding solutions, encountered during the development of the guidance, navigation, and control (GNC) systems for the autonomous underwater vehicles (AUVs) and the autonomous surface vehicles (ASVs) involved in the Widely scalable Mobile Underwater Sonar Technology (WiMUST) project.

This chapter presents a strategy to enable a team of mobile robots to adaptively sample and track a dynamic spatiotemporal process. We propose a distributed strategy where robots collect sparse sensor measurements, create a reduced -order model (ROM) of the spatiotemporal process, and use this model to estimate field values for areas without sensor measurements of the dynamic process. The robots then use these estimates of the field, or inferences about the process, to adapt the model and reconfigure their sensing locations. We use this method to obtain an estimate for the underlying fl ow field and use that to plan optimal energy paths for robots to travel between sensing locations. We show that the errors due to the reduced -order modeling scheme are bounded, and we illustrate the application of the proposed solution in simulation and compare it to centralized and global approaches. We then test our approach with physical marine robots sampling a spatially nonuniform time -varying process in a water tank.

The chapter elaborates on three views on the maritime robotics aspect of AUVs (autonomous underwater vehicles). Summaries, conclusions, and recommendations for future work from the authors of the chapters are collected. The conclusion from a (holistic) viewpoint on autonomy is given. AUVs belong to the class of unmanned or automated systems, which are removing or remotely locating the operator. This reduces cost of the operation, reduces the size of the platform, and removes risk of life because the maritime operation is a naturally harsh environment. In this sense, AUVs can be viewed as maritime robots. However, AUVs can be (much!) more. They can interactively adapt to their environment, which is a sign of autonomy. On-board processing power will allow them to adapt with machine speed. Sufficient processing power is a prerequisite for implementing learning algorithms on board the AUVs in order to improve with experience, but to apply this learning advances in the organisation of the learning space are needed.