This chapter presents the unmanned underwater vehicle (UUV) equations of motion using the results of Fossen. The nonlinear model presented in this chapter is mainly intended for control systems design in combination with system identification and parameter estimation. The resulting model is decoupled into longitudinal and lateral motions such that autopilots for speed, depth/diving and heading control can be designed. The motivation for using nonlinear theory is that one model can cover the whole flight envelope of the UUV, instead of linearising the model about many working points and using gain scheduling between these. In addition, physical model properties in terms of first principles are not destroyed which is a primary problem associated with linearising a plant. Finally, nonlinear optimisation methods are used as a tool for system identification, while proportional-integral-denvative (PID) control and backstepping demonstrate how nonlinear autopilots can be designed.

A control architecture is a key component in the development of an autonomous robot. The control architecture has the goal of accomplishing a mission which can be divided into a set of sequential tasks. This chapter has presented behaviour-based control architectures as a methodology to implement this kind of controller. Its high interaction with the environment, as well as its fast execution and reactivity, are the keys to its success in controlling autonomous robots. The main attention has been given to the coordination methodology. Competitive coordinators assure the robustness of the controller, whereas cooperative coordinators determine the performance of the final robot trajectory. The structure of a control architecture for an autonomous robot has been presented. Two main layers are found in this schema; the deliberative layer which divides the robot mission into a set of tasks, and the behaviour-based layer which is in charge of accomplishing these tasks. This chapter has focused only on the behaviour-based layer. A behaviour coordination approach has been proposed, its main feature being a hybrid coordination of behaviours between competitive and cooperative approaches. A second distinctive part is the use of a learning algorithm to learn the internal mapping between the environment state and the robot actions. Then, the chapter has introduced the main features of the URIS AUV and its experimental set-up. According to the navigation system we have shown that the estimation of the robot position and velocity can be effectively performed with a vision-based system using mosaicking techniques. This approach is feasible when the vehicle navigates near the ocean floor and is also useful to generate a map of the area. Finally, results on real data have been shown through an example in which the behaviour-based layer controlled URIS in a target following task. In these experiments several behaviours were in charge of the control of the robot to avoid obstacles, to find the target and to follow it. One of these behaviours was automatically learnt demonstrating the feasibility of the learning algorithms. Finally, we have also shown the performance of the navigation system using the visual mosaicking techniques.

Underwater vehicles operate in dynamical environments where sudden changes of the working conditions occur from time to time. The need for an effective control action calls for refined techniques with a high degree of robustness with respect to large parametric variations and/or uncertainties. Supervised switching control gives the theoretical framework where appropriate control strategies can be developed. Both the switching algorithms proposed here are based on a multiple models approach to describe the different operating conditions.

A tethered flight vehicle such as Subzero III can be a reliable test-bed for AUV control techniques provided that the tether effects be removed by feed-forward control. To achieve the composite control idea, a numerical scheme for prediction of tether effects has been proposed and assessed. We argue that the composite control scheme can also be applied to the control of an ROV during the deployment of the umbilical. The H_∞ approach has been applied to the design of the autopilots for autonomous underwater vehicles. The autopilots developed have the following features: (1) they have simpler structures than conventional H_∞ design and the performance degrada tion caused by the controller order reduction is negligible; (2) rate feedback is applied for heading and depth control to improve tracking performance; (3) the overshoot specification is considered in the design by choosing suitable weighting functions. The results of both nonlinear simulations and water trials in two different tanks show that the autopilots are robust to model uncertainty, external disturbances and varying dynamics of the vehicle and exhibit good tracking performance. Thus the effectiveness of robust control for AUVs appears promising.

In this chapter, the evolution of the hardware of underwater manipulators will be described by introducing an electromechanical arm of SAUVIM, and some theoretical issues with the arm control system will be discussed, addressing the required robustness in different situations that the manipulator may face during intervention missions. An advanced user interface will then be briefly discussed, which helps to cope with the communication limits and provides a remote programming environment where the interaction with the manipulator is limited only to a very high level. An application example with SAUVIM will be presented before conclusions.

The development of propulsion mechanisms for autonomous underwater vehicles (AUVs) has recently attracted increased attention. However, underwater vehicles that are powered by thrusters and control surfaces which exhibit poor hovering, turning and manoeuvring performance in water currents. Natural selection has ensured that fish have evolved highly efficient swimming mechanisms. Their remarkable swimming abilities can be used as inspiration for innovative designs that improve the man-made systems operating in, and interacting with, aquatic environments.

This chapter has reported the design and development of a real-time human-computer interface to enable a human operator to more effectively utilise the large volume of quantitative data (navigation, scientific and vehicle status data) generated in real time by the sensor suites of underwater robotic vehicles. The system provides an interactive 3D graphical interface that displays, under user control, quantitative spatial and temporal sensor data presently available to pilots and users only as alpha-numerical and 2D displays. The system has been experimentally evaluated based upon a comparison to a ground truth laser scan. The comparison of the accuracy of real-time system to a laser scan has shown a standard deviation of 0.0469 and absolute mean of 0.0041 m. This demonstrates that the system accurately displays data within the capabilities of the sensors. Our experience has shown that it provides improved 3D spatial awareness to the user. Although it is difficult to depict in static images, the real-time spatially accurate display of survey bathymetric and vehicle trajectory data provide the user with an instant comprehension of the progress of the survey, the completeness of the bathy metric sonar coverage, and the quality of the data. The system has been tested on the Woods Hole Oceanographic Jason 2 ROV. These data have shown the feasibility of using the system with at sea oceanographic survey operations.

Autonomous surface craft have been developed in particular for marine research and surveying exploration as well as for the rescue of human life at sea. To provide a rescue vehicle, an autonomous Rescue Dolphin was developed to rescue people in distress. The rescue system automatically triggers the alarm to the ship's management in case of 'man overboard', independently of ship manoeuvres. It moves fast towards the distressed person, and safeguards the distressed person until recovery by ship. The development of the unmanned autonomous surface vehicle Measuring Dolphin was carried out within the framework of the German cooperation project MESSIN. The main task of the project MESSIN consisted of the development and the testing of a prototype of the autonomous surface vehicle Measuring Dolphin which could be applied with high accuracy of positioning and track guidance and under shallow water conditions as a carrier of measuring devices. Fields of application include depth surveying, current and current profile measuring in port entrances and rivers, sediment research, extraction of samples for biological investigations and measuring in drinking water areas.

Autonomous surface and underwater vehicles have been widely developed, but those operating at the interface, submerged just below the surface yet penetrating to the air, have not received the same attention. Yet they combine many of the advantages of both surface and underwater vehicles, but with some less capability than either. This chapter discusses unmanned wave-piercing vehicles, which operate at the interface between sea and air. It will outline their development history, and the two main design approaches evolved so far, together with some detail on the vehicles which have resulted.