The previous volume Advances in Unmanned Marine Vehicles brought together eighteen chapters describing research and developments in unmanned marine vehicles (UMVs). It was observed that almost without exception research groups worldwide were developing and working on real UMVs which means that they are able to test, evaluate and re-evaluate their designs in relatively quick succession, thereby rapidly reporting new approaches, techniques, designs and successes. This rapid design-evaluation cycle is the prime mover for progress, not only for consolidating designs but also leading to new design ideas and innovation. Since its publication in 2006, Advances in Unmanned Marine Vehicles has proven to be a useful and popular source of reference. However, the rapid design-evaluation cycle means further advances have been made which need to be reported. Thus, the seventeen chapters contained in this volume cover further advances in autonomous underwater vehicles, remotely operated vehicles, semi-submersibles, unmanned surface vessels whilst operating autonomously and/or in co-operation with other types of UMV. This book will be of interest to undergraduates, postgraduates, researchers and industrialists who are involved in the design and development of UMVs.
Inspec keywords: sonar tracking; image fusion; SLAM (robots); time-varying systems; mobile robots; path planning; neurocontrollers; autonomous underwater vehicles; adaptive control
Other keywords: dynamic positioning system; multiple autonomous marine vehicles; unmanned marine vehicles; sonar-based simultaneous localization-and-mapping; hybrid glider; ROV LATIS; port-Hamiltonian control; next generation smart underwater vehicle; biomimetic underwater vehicle design concept; neural network-based switching adaptive control; fault-tolerant multisensor navigation system design; dynamic tracking system; vehicle-following; robotic fish
Subjects: Time-varying control systems; Self-adjusting control systems; General and management topics; Marine system control; Telerobotics; Computer vision and image processing techniques; Spatial variables control; Mobile robots; Neurocontrol
This chapter described the main features of ROV LATIS, the next generation smart remotely operated underwater vehicle. The vehicle was built as a prototype platform to demonstrate system validity and operability and to prove new technologies developed in the Mobile & Marine Robotics Research Centre, UL. It is a next generation smart ROV with unique features, including multiple modes of operation, advanced 2D and 3D displays, intuitive and easy to use pilot interface and fault-tolerant control system.
The philosophy behind the HyBIS instrument is to develop a technological solution that addresses the specific requirements of the user. Rather than provide a comprehensive engineering capability in a single system (e.g. an ROV), we used the specific scientific needs of the user to inform the design and development of the technology. The result is surprising: a versatile technology, modular in design, that has a low capital cost and is relatively easy to operate. We believe that this approach is both efficient and cost effective. For a fraction of the capital and running cost of a conventional ROV, the HyBIS system meets many of the users' needs. Ensuring that excess capacity and common formats are designed in from the start, the instrument can be easily expanded and developed to meet future requirements. Key to the success of this approach is knowing exactly what the user needs and distinguishing this from what the user thinks they want. This requires a bi-lateral process of information flow: educating both the engineer and the user so that each knows what can be achieved and what is actually required.
This chapter deals with the problem of AUV modelling and hydrodynamic coefficient estimation in the context of the Pirajuba project. The Pirajuba is an AUV, which is being developed as a test bed for investigations on dynamics and navigation of this class of vehicle.
Underwater vehicles operate in dynamic environments where sudden changes of the working conditions occur from time to time. The need of 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 seems to be theoretical frameworks where appropriate control strategies can be developed. In this chapter, a switching adaptive tracking control based on NNs is proposed and compared with an NN-based switching controller. The form of used nets is the RBFN, which has been used successfully in other control system applications (Antonini et al., 2006) and has favourable characteristics in terms of the best approximation property (Poggio & Girosi, 1990). On the basis of numerical tests, the NNSAC is able to cope with the large transient errors related to the considered mode-switch processes, when knowledge of the different possible vehicle configurations is poor. In fact, if the operative conditions are unknown, an NNSC cannot guarantee good control performance; the pre-computed controllers cannot cope with all environment and load conditions. Therefore, the integration of a switching control strategy with adaptive controllers is particularly well suited to cope with these unknown operative conditions and improve the performance of the overall control system when the different environments where the vehicle operates are not well known.
In this chapter the newly developed DP control system implemented on the ROV Minerva is presented. The selected hardware and software platforms were LabView and National Instruments cRIO. The DP control system was made up by modules for signal processing, guidance, observer for filtering and state estimation, controller, supervisor and adaption, and thrust allocation. The DP control system was interfaced to a navigation system and local thruster controllers. Experimental results with a nonlinear output feedback controller for DP based on extended Kalman filter and nonlinear PID controller were presented.
This chapter presents a novel control strategy for trajectory tracking of underwater marine vehicles that are designed using port-Hamiltonian theory. A model for neutrally buoyant underwater vehicles is formulated as a PHS, and then the tracking controller is designed for the horizontal plane-surge, sway and yaw. The control design is done by formulating the error dynamics as a set-point regulation port-Hamiltonian control problem. The control design is formulated in two steps. In the first step, a static-feedback tracking controller is designed, and the second step integral action is added. The global asymptotic stability of the closed loop system is proved and the performance of the controller is illustrated using a model of an open-frame offshore underwater vehicle.
During the last decades, a number of studies in mobile robotics have developed techniques to address the localization problem with very promising results. In particular, the SLAM techniques have been broadly and successfully applied to indoor and outdoor environments. As electromagnetic signals suffer from strong attenuation underwater, the use of ultra-high-resolution devices, like vision or laser scanners, is impractical for long-range sensing. For this reason, in this chapter we focused on the underwater sonar-based SLAM techniques that utilize sonar as main measurement sensor, since they can perceive inside the water further than vision. We surveyed the recent advances of the underwater sonar-based SLAM by reviewing the state of the art, before proposing a method based on scan matching. A comparison between the most relevant scan-matching algorithms in 2D revealed that the probabilistic scan-matching algorithm pIC seems to work better with sonar data. According to this, we proposed a sonar-based SLAM algorithm that first corrects the motion-induced distortions in the scans and then uses the pIC for registering adjacent scans to estimate the displacements of the vehicle, as well as loop closures. Finally, an augmented state EKF estimates and keeps the registered scans poses. No a priori structural information or initial poses are considered. The algorithm has been tested with a real-world dataset showing the viability of the proposed approach.
In this chapter Teleo-Reactive Executive (T-REX) is designed, developed, tested and deployed as an onboard adaptive control system that integrates artificial intelligence (AI)-based planning and probabilistic state estimation in a hybrid executive. Probabilistic state estimation integrates a number of science observations to produce a likelihood that the vehicle sensors perceive a feature of interest. Onboard planning and execution enable adaptation of navigation and instrument control based on the probability of having detected such a phenomenon. It further enables goal-directed commanding within the context of projected mission state and allows for replanning for off-nominal situations and opportunistic science events.
This chapter presents experimental and theoretical issues related to the cooperative path-following of unmanned marine vehicles (UMVs) focusing on the application of vehicle-following and methodological aspects regarding the design of suitable metrics for performance evaluation of the proposed guidance techniques. Unmanned surface vehicles (USVs), thanks to their position at the air-sea interface, can play a key role in the networks of heterogeneous manned/unmanned air, ground and marine platforms that characterize the next-century scenarios of environmental monitoring, border surveillance, warfare and defence applications. Indeed, they can act as communication nodes connecting radio frequency networks in air and acoustic ones undersea, monitoring ocean and atmosphere dynamics as well as surface and underwater intrusions. In particular, USVs are naturally seen as a part of flotillas of heterogeneous vehicles executing large-scale surveys and supporting rapid environmental assessment (REA), and an increasing number of prototype vehicles have been developed for science, bathymetric mapping, defence and general robotics research.
This chapter highlights in detail the development of a USV named Springer. The hardware and software architecture of Springer including its sensors suite are discussed in detail. Initial experiments to collect data for modelling purpose are outlined. Details regarding the design of its advanced navigation, guidance and control systems are also presented.
This chapter gives a brief overview of some of the theoretical and practical issues that arise in the process of developing advanced motion control systems for cooperative multiple AMVs. Many of the problems addressed were motivated by challenging scientific mission scenarios defined in the course of the EU GREX and Co3-AUVs projects. A general architecture for cooperative AMV control in the presence of timevarying communication topologies and communication losses was developed. The architecture implementation relies on a number of SVPs and MVPs, the development of which was rooted in solid control theory. For simulation purposes, a proprietary Networked Marine Systems Simulator (NetMarSyS) was developed at ISR/IST. The simulator allows for the study of the performance that can be achieved with the algorithms developed for cooperative motion control, with due account for full vehicle dynamics, external disturbances and sensor noise, as well as inter-vehicle communication losses. NetMarSyS allows also for seamless distributed software and hardware-in-the-loop (HIL) simulations, prior to systems deployment at sea. The results of two test series carried out in the Azores and in Sesimbra, Portugal have shown the efficacy of the methods developed for cooperative motion control, as well as the reliability of the software and hardware structures that are currently being used to control the Medusa class of ASVs. Future work will address testing other MVPs (including the Go-To-Formation and Cooperative Target Tracking) and performing missions whereby a number of surface vehicles are used to track multiple targets underwater. From a theoretical standpoint, two main lines of research are envisioned: (1) cooperative navigation exploiting non-conventional geophysical-based navigation systems, and (2) in-depth study of the constraints imposed by the underwater channel and underwater communication protocols.
An auxiliary propulsion module for a 200-m Slocum glider has been presented.The module greatly enhances the operational abilities of underwater gliders by enabling horizontal flight and increased speed capabilities. A simplified hydrodynamic model under the assumption of a zero angle of attack was used to design the module. Using this model, the initial component selection was accomplished through matching the motor, gearbox and measured propeller efficiencies. The propulsion module's performance was confirmed through propulsion tests in the flume tank at MUN.
The chapter describes the development of a payload-carrying capability on the Folaga vehicles (eFolaga). In the eFolaga design, lightweight, small dimensions, low-cost characteristics have all been maintained, as well as high manoeuvrability and hovering capacities. A general methodology to derive lumped parameter models of eFolaga like vehicles has been described, where the identification of the eFolaga buoyancy change and mass displacement actuators has also been reported. By judicious design, it is possible to lift the eFolaga modularity also at the software level, and indeed to have intelligent payloads implementing specific autonomous behaviours setting up a dialogue with the native eFolaga GNC.
This chapter presents an overview of the work on the RoboSalmon prototype biomimetic underwater vehicle carried out at the University of Glasgow. This work includes the development of the mathematical model that covers the kinematics and dynamics of the RoboSalmon vehicle to assist with the understanding of the dynamics of the swimming process. Details of the method used to model the tendon drive propulsion system are presented along with details of the modelling of the recoil motion. Experimental surge results are presented, which show a number of trends in the data including an increase in surge velocity with increasing tail-beat frequency. The maximum surge velocity obtainable from the vehicle before actuator saturation occurred was 0.18 m/s, which was achieved at a tail-beat frequency 0.61 Hz and a nominal tail-beat amplitude 0.15 m. From the surge data collected, the relationship between tail-beat frequency and surge velocity appeared to be linear. The range of surge velocities obtained for the RoboSalmon was then compared to the swimming performance of a real Salmon, which showed that the swimming speed of the RoboSalmon obtained for a particular beat frequency was lower than that achievable by a real Salmon by around a factor of 3.2. This difference in performance is due to the mechanical nature of the RoboSalmon system. Overall, the work completed on the RoboSalmon has shown that a biomimetic fish-like propulsion system is potentially viable as a form of propulsion for an AUV. The experimental results show that the biomimetic system used on the RoboSalmon may have advantages over a conventional propeller- and rudder-based system in terms of improved propulsive efficiencies and increased vehicle manoeuvrability. Further investigation and development of this technology could lead to the development of AUVs with significantly increased efficiencies and manoeuvrability thus allowing longer and more challenging missions to be undertaken.
Bio-manoeuvring-type underwater vehicles (robotic fish) have been developed for needs related to mineral resources, ocean structure maintenance and environmental measurement. These robotic fishes are propelled by 'fin' movement, like fish and other aquatic life. Utilizing the latest dynamic system theory and mechatronics technology, a flexible oscillating fin propulsion system was built to realize flexible movement of fish. Furthermore, advanced autonomous control logic was developed to create a life-like swimming robotic fish capable of three-dimensional autonomous movement without cables.
Optimum cost of transport provides a useful metric for comparing the total energetic requirements of marine animals and AUV. This chapter uses a system approach to explain why many AUV have a lower cost of transport than marine animals of similar size.