Unmanned marine vehicles (UMVs) include autonomous underwater vehicles, remotely operated vehicles, semi-submersibles and unmanned surface craft. Considerable importance is being placed on the design and development of such vehicles, as they provide cost-effective solutions to a number of littoral, coastal and offshore problems. This book highlights the advanced technology that is evolving to meet the challenges being posed in this exciting and growing area of research.
Inspec keywords: multi-robot systems; collision avoidance; remotely operated vehicles; underwater vehicles; mobile robots; motion control
Other keywords: tethered flight vehicle verification; obstacle avoidance; spatiotemporal 3D data visualisation; multiple autonomous marine robot; ROV; unmanned surface vehicle; motion control; underwater glider; unmanned marine vehicle control
Subjects: Marine system control; Robotics; Spatial variables control
This book covers aspects of navigation guidance and control of UMVs. A wide range of vehicle types and applications are presented, together with a wide range of innovative approaches and techniques for enhancing the performance of UMVs. There are many challenges for the future and whilst it is difficult to predict in which particular areas, and when, advance will be made, it is clear that improvements in a number of areas are imminent. In particular advances in areas such as underwater communications, battery technology, fuel cells, propulsion systems, autonomous underwater docking, sensor fusion and swarms (cooperative UMVs) will impact on the future use and deployment of UMVs. Guidance and control of UMVs and associated technologies is therefore a very active and productive research area, and it is clear that significant advances will be made.
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.
In the context of autonomy for underwater vehicles, we assume that a usual suite of feedback controllers are present in the form of autopilot functions that provide for the regulation of vehicle speed, heading and depth or altitude. In this chapter. we consider the topic of guidance laws, obstacle avoidance and the use of artificial potential functions (APFs). This topic deals with the computations required to plan and develop paths and commands, which are used by these autopilots. Simple guidance laws such as 'proportional guidance' have been used for many years in missiles to provide interception with targets. Lateral accelerations are commanded proportional to the rate of change of line of sight. So long as the chaser vehicle has a speed advantage over the non-manoeuvring target, simply reducing the angle of line of sight (LOS) to zero will result in an interception. For applications with unmanned underwater vehicles, guidance laws allow vehicles to follow paths constructed in conjunction with mission objectives.
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.
This chapter introduces a new hybrid approach associated with the thmster control allocation problem for over-actuated thruster-propelled open-frame underwater vehicles (UVs). The work presented herein is applicable to a wide class of control allocation problems, where the number of actuators is higher than the number of objectives. However, the application described here is focused on two remotely operated vehicles (ROVs) with different thruster configuration.
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.
The development of autonomous underwater vehicles (AUVs) for scientific, military and commercial purposes in applications such as ocean surveying, unexploded ordnance hunting and cable tracking and inspection requires the correspond ing development of navigation, guidance and control (NGC) systems, which should work in accord with each other for proper operation. Navigation systems are necessary to provide knowledge of vehicle position and attitude. The guidance systems manipulate the output of the navigation systems to generate suitable trajectories to be followed by the vehicle. This takes into account the target and any obstacles that may have been encountered during the course of a mission. The control systems are responsible for keeping the vehicle on course as specified by the guidance processor. In the Hammerhead AUV, this is achieved through manipulating the rudder and the hydroplanes (canards) of the vehicle. The need for accuracy in NGC systems is paramount. Erroneous position and attitude data in navigation systems can lead to a meaningless interpretation of the collected data, which in turn affects the accuracy of the corresponding guidance and control systems. This, if not contained properly may lead to a catastrophic failure of an AUV during a specific mission. The integrated NGC of the Hammerhead AUV is depicted.
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.
This chapter focuses on the problem of developing a low-cost station-keeping system for remotely operated vehicles (ROVs) that is able to handle external disturbances, as well as sea currents and tether forces, and uncertainty in system dynamics. This uncertainty includes poor knowledge of hydrodynamic derivatives, sensor measure ments, that is, noise and low sampling rate, and actuator forces, which are generally affected by propeller-propeller and propeller-hull interactions.
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.
Starting by simply lowering sensing equipment into the ocean from boats, the development of new technology and methods to gain access to the ocean has led to the birth of submarines, remotely operated vehicles (ROVs) and under water observation stations connected to land via cables. These advancements have made possible observations that were unimaginable 100 years ago. Now, autonomous underwater vehicles (AUVs) are being introduced as a new observation platform.
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.
It has been shown by experiment that the vision sensor as detailed in this chapter, whilst simultaneously acquiring bathymetry and reflectivity profiles, can derive robust altitude data at extended ranges (4-8 m) over extended image sequences using laser tπangulation alone to an accuracy of between 1 and 5 per cent. In addition, it is possible to utilize the unused portion of the CCD image to determine horizon tal motion by means of a region-based tracker. The combined measurements from the region-based tracker and the laser triangulation sensor can produce a stand-alone accuracy of around 97 per cent of the distance travelled at an altitude of 4 m in filtered seawater. This result is subject to degradation as the turbidity of the water increases, the velocity and altitude increase and the planar nature of the tank bottom is reduced. Finally, as well as deriving navigational information, the motion detected can be used to modify contrast enhanced composite waterfall images such that they are spatially representative of the actual seabed scene.
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.
The last 15 years have seen a great deal of well-publicised research and development of unmanned underwater vehicles (UUVs) throughout the world. The naval requirements for stealthy reconnaissance in the littoral and the possibilities of driving down the costs of civil seabed survey operations have combined with the intrinsically interesting technical challenges of producing cost effective UUVs to generate a vibrant research community.
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.
The chapter addresses the topics of marine vehicle and mission control from both a theoretical and a practical point of view. The presentation is rooted in practi cal developments and experiments carried out with the Delfim and Caravela ASCs, and the Infante and Sirene AUVs. Examples of mission scenarios with the above vehicles working alone or in cooperation set the stage for the main contents of the chapter.
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.
An underwater glider is a winged, buoyancy-driven AUV developed especially to autonomously collect oceanographic data over the course of weeks or months at a time. The standard, on-board, oceanographic measurements include conductivity, temperature and pressure, but other sensors such as bathy-photometers, optical backscatter sensors, fluorometers, photosynthetically active radiation (PAR) sensors and various acoustic sensors have been successfully tested and deployed on gliders. Henry Stommel and Douglas C. Webb, initially conceived the glider concept envisioning an endurance vehicle that could sample the ocean while circumnavigating the globe.
