Robust Controller Design for an Autonomous Underwater Vehicle

Worldwide there has been a surge of interest in Autonomous Underwater Vehicles (AUV). The ability to operate without human intervention is what makes this technology so appealing. On the other hand, the absence of the human narrows the AUV operation to its control system, computing, and sensing capabilities. Therefore, devising a robust control is mandatory to allow the feasibility of the AUV. Motivated by this fact, this thesis aims to present, discuss and evaluate two linear control solutions being proposed for an AUV developed by a consortium led by CEiiA. To allow the controller design, the dynamic model of this vehicle and respective considerations are firstly addressed. Since the purpose is to enable the vehicle’s operation, devising suitable guidance laws becomes essential. A simple waypoint following and station keeping algorithm, and a path following algorithms are presented. To devise the controllers, a linear version of the dynamic model is derived considering a single operational point. Then, through the decoupling of the linear system into three lightly interactive subsystems, four Proportional Integral Derivative controllers (PIDs) are devised for each Degree Of Freedom (DOF) of the vehicle. A Linear Quadratic Regulator (LQR) design, based on the decoupling of the linear model into longitudinal and lateral subsystems is also devised. To allocate the controller output throughout the actuators, a control allocation law is devised, which improves maneuverability of the vehicle. The results present a solid performance for both control methods, however, in this work, LQR proved to be slightly faster than PID.É visível, a nível mundial, um aumento considerável do interesse em Veículos Autónomos Subaquáticos (Autonomous Underwater Vehicles - AUV). O que torna esta tecnologia tão atraente é a capacidade de operar sem intervenção humana. Contudo, a ausência do ser humano restringe a operação do AUV ao seu sistema de controlo, computação e capacidades de detecção. Desta forma, conceber um controlo robusto é obrigatório para viabilizar o AUV. Motivado por este facto, esta tese tem como objetivo apresentar, discutir e avaliar duas soluções de controlo linear, a propor a um AUV desenvolvido por um consórcio liderado pelo CEiiA. Para que o projeto do controlador seja possível, o modelo dinâmico deste veículo e respectivas considerações são primeiramente abordados. Com a finalidade de possibilitar a operação do veículo, torna-se essencial a elaboração de leis de guidance adequadas. Para este efeito são apresentados algorítmos de Waypoint following e Station keeping, e de path following. Para a projeção dos controladores é derivada uma versão linear do modelo dinâmico, considerando um único ponto operacional. Através da separação do modelo linear em três subsistemas são criados quatro controladores Proporcional Integral Derivativo (PID) para cada grau de liberdade (Degree Of Freedom - DOF) do veículo. É também projetado um Regulador Linear Quadrático (LQR), baseado na separação do modelo linear em dois subsistemas, longitudinal e lateral. É ainda apresentada uma lei de alocação de controlo para distribuir o sinal de saída dos controladores pelos diferentes atuadores. Esta provou melhorar a manobrabilidade do veículo. Os resultados finais apresentam um desempenho sólido para ambos os métodos de controlo. No entanto, neste trabalho, o LQR provou ser mais rápido do que o PID

Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel

This paper describes the design, implementation and testing of a suite of algorithms to enable depth constrained autonomous bathymetric (underwater topography) mapping by an Autonomous Surface Vessel (ASV). Given a target depth and a bounding polygon, the ASV will find and follow the intersection of the bounding polygon and the depth contour as modeled online with a Gaussian Process (GP). This intersection, once mapped, will then be used as a boundary within which a path will be planned for coverage to build a map of the Bathymetry. Methods for sequential updates to GP's are described allowing online fitting, prediction and hyper-parameter optimisation on a small embedded PC. New algorithms are introduced for the partitioning of convex polygons to allow efficient path planning for coverage. These algorithms are tested both in simulation and in the field with a small twin hull differential thrust vessel built for the task.Comment: 21 pages, 9 Figures, 1 Table. Submitted to The Journal of Field Robotic

Boundary tracking and source seeking of oceanic features using autonomous vehicles

The thesis concerns the study and the development of boundary tracking and source seeking approaches for autonomous vehicles, specifically for marine autonomous systems. The underlying idea is that the characterization of most environmental features can be posed from either a boundary tracking or a source seeking perspective. The suboptimal sliding mode boundary tracking approach is considered and, as a first contribution, it is extended to the study of three dimensional features. The approach is aimed at controlling the movement of an underwater glider tracking a three-dimensional underwater feature and it is validated in a simulated environment. Subsequently, a source seeking approach based on sliding mode extremum seeking ideas is proposed. This approach is developed for the application to a single surface autonomous vehicle, seeking the source of a static or dynamic two dimensional spatial field. A sufficient condition which guarantees the finite time convergence to a neighbourhood of the source is introduced. Furthermore, a probabilistic learning boundary tracking approach is proposed, aimed at exploiting the available preliminary information relating to the spatial phenomenon of interest in the control strategy. As an additional contribution, the sliding mode boundary tracking approach is experimentally validated in a set of sea-trials with the deployment of a surface autonomous vehicle. Finally, an embedded system implementing the proposed boundary tracking strategy is developed for future installation on board of the autonomous vehicle. This work demonstrates the possibility to perform boundary tracking with a fully autonomous vehicle and to operate marine autonomous systems without remote control or pre-planning. Conclusions are drawn from the results of the research presented in this thesis and directions for future work are identified

Task-space dynamic control of underwater robots

This thesis is concerned with the control aspects for underwater tasks performed by marine robots. The mathematical models of an underwater vehicle and an underwater vehicle with an onboard manipulator are discussed together with their associated properties. The task-space regulation problem for an underwater vehicle is addressed where the desired target is commonly specified as a point. A new control technique is proposed where the multiple targets are defined as sub-regions. A fuzzy technique is used to handle these multiple sub-region criteria effectively. Due to the unknown gravitational and buoyancy forces, an adaptive term is adopted in the proposed controller. An extension to a region boundary-based control law is then proposed for an underwater vehicle to illustrate the flexibility of the region reaching concept. In this novel controller, a desired target is defined as a boundary instead of a point or region. For a mapping of the uncertain restoring forces, a least-squares estimation algorithm and the inverse Jacobian matrix are utilised in the adaptive control law. To realise a new tracking control concept for a kinematically redundant robot, subregion tracking control schemes with a sub-tasks objective are developed for a UVMS. In this concept, the desired objective is specified as a moving sub-region instead of a trajectory. In addition, due to the system being kinematically redundant, the controller also enables the use of self-motion of the system to perform sub-tasks (drag minimisation, obstacle avoidance, manipulability and avoidance of mechanical joint limits)

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT

Adaptive Sampling For Efficient Online Modelling

This thesis examines methods enabling autonomous systems to make active sampling and planning decisions in real time. Gaussian Process (GP) regression is chosen as a framework for its non-parametric approach allowing flexibility in unknown environments. The first part of the thesis focuses on depth constrained full coverage bathymetric surveys in unknown environments. Algorithms are developed to find and follow a depth contour, modelled with a GP, and produce a depth constrained boundary. An extension to the Boustrophedon Cellular Decomposition, Discrete Monotone Polygonal Partitioning is developed allowing efficient planning for coverage within this boundary. Efficient computational methods such as incremental Cholesky updates are implemented to allow online Hyper Parameter optimisation and fitting of the GP's. This is demonstrated in simulation and the field on a platform built for the purpose. The second part of this thesis focuses on modelling the surface salinity profiles of estuarine tidal fronts. The standard GP model assumes evenly distributed noise, which does not always hold. This can be handled with Heteroscedastic noise. An efficient new method, Parametric Heteroscedastic Gaussian Process regression, is proposed. This is applied to active sample selection on stationary fronts and adaptive planning on moving fronts where a number of information theoretic methods are compared. The use of a mean function is shown to increase the accuracy of predictions whilst reducing optimisation time. These algorithms are validated in simulation. Algorithmic development is focused on efficient methods allowing deployment on platforms with constrained computational resources. Whilst the application of this thesis is Autonomous Surface Vessels, it is hoped the issues discussed and solutions provided have relevance to other applications in robotics and wider fields such as spatial statistics and machine learning in general

Gait identification and optimisation for amphi-underwater robot by using ant colony algorithm

Manoeuvrable robot commonly has become the focus of the latest heated issues especially in applications that involved disaster rescue, military missions and underwater or extra-terrestrial explorations. Currently, the manoeuvrable robot is controlled manually by the operator and it’s a wheeled type. It is used for rescue missions to transport people from disaster area to the safe zone. However, the robot is incapable of moving automatically, and it goes through terrain or landscape like swarm. Therefore, a suitable platform is required to transport or for other uses especially in dangerous mission. It is very difficult to estimate the movement of the robot to avoid obstacles and choose the alternative path. Hence, this research presents the point-to-point gait identification or path planning of the behavious of the robot to manuever autonomously on both on-land and underwater environment. For the optimization, the robot will travel from one specific point to another with the predefined position within optimized gait and fastest time by using Ant Colony Optimization (ACO) technique. The algorithm being compared, between Ant Colony Algorithm (ACO) and the Particle Swarm Optimisation (PSO) in terms of time and distance. The ACO been chosen because of the positive feedback for rapid discovery and able to use in dynamic applications for example adapts to changes like new distances. The performance of the algorithm showed that the execution time of ACO is more realistic. Hence, Matlab is used to determine the best cost extracted from the ACO with the pre-define of number of iteration and the number of ants. The laboratory-scaled prototype for amphibious vehicle was developed to test the design controlled with ACO technique where Global Positioning System (GPS) is used for the coordination of the robot and Magnetometer for the position of the robot. The robot prototype is able to move autonomously and optimized by the ant colony optimization with predefined position and terrain condition © BEIESP