Acoustic Theory Application in Ultra Short Baseline System for Tracking AUV

The effective tracking area of ultra short baseline (USBL) systems strongly relates to the safety of autonomous underwater vehicles (AUVs). This problem has not been studied previously. A method for determining the effective tracking area using acoustic theory is proposed. Ray acoustic equations are used to draw rays, which ascertain the effective space. The sonar equation is established in order to discover the available range of the USBL system and the background noise level using sonar characteristics. The available range defines a hemisphere like enclosure. The overlap of the effective space with the hemisphere is the effective area for USBL systems tracking AUVs. Lake and sea trials show the proposed method's validity.     

A Cascade Approach for Global Trajectory Tracking Control of Underactuated AUVs

A global trajectory tracking controller is presented for underactuated AUVs with only surge force and yaw moment in the horizontal plane. A transformation is introduced to represent the tracking error system into a cascade form. The global and uniform asymptotic stabilization problem of the resulting cascade system is reduced to the stabilization problem of two subsystems by use of the cascade approach. For the stabilization of the subsystem involving the yaw moment, a control law is proposed based on the feedback linearization method. Another subsystem is stabilized by designing a fuzzy sliding mode controller which can offer a systematical means of constructing a set of shrinking-span and dilating-span membership functions. In order to demonstrate the practicability of the proposed controller, control constraints, parameter uncertainties, and external disturbances are considered according to practical situation of AUVs. Simulation results show very good tracking performance and robustness of the proposed control schemes.     

A Numerical and Experimental Study on the Hull-Propeller Interaction of A Long Range Autonomous Underwater Vehicle

Range is an important factor to the design of autonomous underwater vehicles(AUVs), while drag reduction efforts are pursued, the investigation of body-propeller interaction is another vital consideration. We present a numerical and experimental study of the hull-propeller interaction for deeply submerged underwater vehicles, using a proportionalintegral-derivative(PID) controller method to estimate self-propulsion point in CFD environment. The hydrodynamic performance of hull and propeller at the balance state when the AUV sails at a fixed depth is investigated, using steady RANS solver of Star-CCM+. The proposed steady RANS solver takes only hours to reach a reasonable solution. It is more time efficient than unsteady simulations which takes days or weeks, as well as huge consumption of computing resources. Explorer 1000, a long range AUV developed by Shenyang Institute of Automation, Chinese Academy of Sciences, was studied as an object, and self-propulsion point, thrust deduction,wake fraction and hull efficiency were analyzed by using the proposed RANS method. Behind-hull performance of the selected propeller MAU4-40, as well as the hull-propeller interaction, was obtained from the computed hydrodynamic forces. The numerical results are in good qualitative and quantitative agreement with the experimental results obtained in the Qiandao Lake of Zhejiang province, China.     

Bistatic synthetic aperture imaging of proud and buried targets from an AUV

The use of autonomous underwater vehicles (AUVs) for the detection of buried mines is an area of current interest to the Mine CounterMeasures (MCM) community. AUVs offer the advantages of lower cost, stealth, reduced operator risk, and potentially improved coverage rates over more traditional mine hunters. However, AUVs also come with their own set of difficulties, including significant error in navigation and low communication rates with the mother platform and each other. In the case of bistatic detection scenarios, AUVs will therefore have difficulty knowing where exactly in space they are and the trigger time of sources on other platforms, be they ships or other AUVs. However, the potential improvement in detection and coverage rates offered by bistatic sonar concepts makes resolution of these issues a high priority. In this paper, the problems of inaccurate navigation and source timing information are addressed for the Generic Oceanographic Array Technology data set. In this experiment, conducted off Marciana Marina during June 1998, a MIT AUV with a SACLANTCEN acoustic array and acquisition system was used together with a TOPAS parametric sonar to explore issues of buried target detection using AUVs. In this paper, solutions to the navigation and timing problems are proposed which enable the effective use of bistatic synthetic aperture sonar (SAS) concepts for the detection of buried objects in the mid-frequency regime of 2-20 kHz.     

Transformed equations of motion for underwater vehicles

A method for dynamics investigation and coupling detection between velocities of autonomous underwater vehicles (AUVs) is presented in this paper. The method is based on transformation of equations of motion, which are usually used for an underwater vehicle, into equations with a diagonal mass matrix. The obtained equations contain quasi-velocities and allow one to give a further insight into the AUV dynamics especially for an underactuated system. Some advantages of the proposed approach are discussed, too. An analytical example for a 3-DOF AUV shows possible application of the transformed equations. Moreover, the given approach is validated via simulation on a 6-DOF vehicle.     

Zero-G Class Underwater Robots: Unrestricted Attitude Control Using Control Moment Gyros

The "Zero-G" is designated as a new class of underwater robot that is capable of unrestricted attitude control. A novel control scheme based on internal actuation using control moment gyros (CMGs) is developed to provide Zero-G class autonomous underwater vehicles (AUVs) with this unique freedom in control. This is implemented in the CMG-actuated Zero-G class internal kinematic underwater robot actuation (IKURA) system that was developed as part of this research. A series of experiments are performed to demonstrate the practical application of CMGs and verify the associated theoretical developments. The ability to actively stabilize the translational dynamics of the robot is assessed and unrestricted attitude control is demonstrated in an experiment that involves vertically pitched diving and surfacing in surge. Finally, potential applications for Zero-G class AUVs are discussed.     

On the Use of Adaptive/Integral Actions for Six-Degrees-of-Freedom Control of Autonomous Underwater Vehicles

In this paper, the control of autonomous underwater vehicles (AUVs) in six-degrees-of-freedom (6-DOFs) is analyzed in a comparison study among several controllers. At steady state, the vehicle needs to compensate for two dynamic effects, the ocean current and the restoring forces; the appropriateness of the adaptive/integral action designed with respect to the persistent effects is discussed. Moreover, for each controller, an adaptive/integral proportional derivative (PD) plus gravity compensation-like version is derived and eventually modified so as to achieve null steady-state error under modeling uncertainty and presence of ocean current. Numerical simulations are presented to better illustrate the controllers' behavior.     

Maneuvering modeling and simulation of AUV dynamic systems with Euler-Rodriguez quaternion method

The purpose of this study is to develop maneuvering models and systems of a simulator to improve the motion performance of autonomous underwater vehicles (AUVs) at the preliminary design stages in advance. The AUVs simulation systems based on the standard submarine equations of motion in six-degree-of-freedom (6-DOF) integrated with the Euler-Rodriguez quaternion method for representing singularity-free AUV attitude and time-saving calculation, and with a nonlinear control model for maneuvering and depth control simulations, time-marching in the fourth-order Runge-Kutta scheme. For validation of the simulation codes, results of the ISiMI AUV open-loop tests including turning test and zigzag test as well as an AUV simulator on the basis of Euler-angle method were used to compare with the quaternion-based AUV simulator. The computational results from the proposed simulator agree well with those from both the ISiMI AUV experiments and the Euler-angle based simulations. Additionally, a new maneuvering procedure, namely "put-out" was implemented to test directional stability for a large-scale AUV in the proposed AUV simulator that can be considered for vehicles in space as well as in constrained planes.     

基于高阶滑模控制器的水下潜器运动控制

水下环境复杂多变,由于水流的不可预知性和多变性,潜器的水动力系数往往无法准确获取,使得依赖这种参数的潜器运动控制算法的应用受到了很大的局限。为了解决控制器对模型参数的依赖,设计了一种基于高阶滑模控制算法的模型无关控制器,并通过设置合理的过渡过程,解决了这种控制算法依赖初值的弊端。仿真结果表明,位置和姿态的控制能够快速的收敛,误差很小并且不依赖于初始条件,控制器需调节参数很少,并且算法简单,适用于工程的实际需要。     

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q/spl I.bar/learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q/spl I.bar/learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs.     

水下自主机器人水动力预报方法研究

针对水下机器人操纵性优化设计中水动力系数预报问题,在水下机器人水动力预报中引入艇体肥瘦指数概念,确定了水下机器人艇体几何描述的五参数模型。提出采用小波神经网络方法预报水下机器人水动力,确定了神经网络的结构,利用均匀试验设计方法,设计了神经网络的学习样本。研究结果表明,只要确定适当的输入参数,选择适当的学习样本和网络结构,利用小波神经网络方法对水下机器人水动力进行预报可以达到较好的精度。     

Robust trajectory control of underwater vehicles using time delay control law

A new control scheme for robust trajectory control based on direct estimation of system dynamics is proposed for underwater vehicles. The proposed controller can work satisfactorily under heavy uncertainty that is commonly encountered in the case of underwater vehicle control. The dynamics of the plant are approximately canceled through the feedback of delayed accelerations and control inputs. Knowledge of the bounds on uncertain terms is not required. It is shown that only the rigid body inertia matrix is sufficient to design the controller. The control law is conceptually simple and computationally easy to implement. The effectiveness of the controller is demonstrated through simulations and implementation issues are discussed.     

Hull hydrodynamic optimization of autonomous underwater vehicles operating at snorkeling depth

Traditionally autonomous underwater vehicles (AUVs) have been built with a torpedo-like shape. This common shaping is hydrodynamically suboptimal for those AUVs required to operate at snorkeling condition near the free surface. In this case, the wave resistance associated to the wavy deformation of the sea surface induced by the motion of the platform is an important component of the drag. This work has investigated the optimum hull shape of an underwater vehicle moving near the free surface. Specifically a first-order Rankine panel method has been implemented to compute the wave resistance on a body of revolution moving close to the free surface. A simulated annealing algorithm was then employed to search those set of parameters defining the hull shape that minimize the wave resistance. The optimization was constrained to keep constant the total volume of the vehicle. The total drag of scaled models of the torpedo-like and resulting optimum shapes was measured in the naval tank of the University of Trieste. Measurements showed a smaller resistance of the optimized shape in the range of the considered Froude numbers.     

Investigation of a method for predicting AUV derivatives

The paper addresses the problem of autonomous underwater vehicle (AUV) modelling and parameter estimation as a means to predict the dynamic performance of underwater vehicles and thus provide solid guidelines during their design phase. The use of analytical and semi-empirical (ASE) methods to estimate the hydrodynamic derivatives of a popular class of AUVs is discussed. A comparison is done with the results obtained by using computational fluid dynamics to evaluate the bare hull lift force distribution around a fully submerged body. An application is made to the estimation of the hydrodynamic derivatives of the MAYA AUV, an autonomous underwater vehicle developed under a joint Indian-Portuguese project. The estimates obtained were used to predict the turning diameter of the vehicle during sea trials.     

Multi-AUV Control and Adaptive Sampling in Monterey Bay

Operations with multiple autonomous underwater vehicles (AUVs) have a variety of underwater applications. For example, a coordinated group of vehicles with environmental sensors can perform adaptive ocean sampling at the appropriate spatial and temporal scales. We describe a methodology for cooperative control of multiple vehicles based on virtual bodies and artificial potentials (VBAP). This methodology allows for adaptable formation control and can be used for missions such as gradient climbing and feature tracking in an uncertain environment. We discuss our implementation on a fleet of autonomous underwater gliders and present results from sea trials in Monterey Bay in August, 2003. These at-sea demonstrations were performed as part of the Autonomous Ocean Sampling Network (AOSN) II project     

Underwater autonomous manipulation for intervention missions AUVs

Many underwater intervention tasks are today performed using manned submersibles or remotely operated vehicles in teleoperation mode. Autonomous underwater vehicles are mostly employed in survey applications. In fact, the low bandwidth and significant time delay inherent in acoustic subsea communications represent a considerable obstacle to remotely operate a manipulation system, making it impossible for remote controllers to react to problems in a timely manner.Nevertheless, vehicles with no physical link and with no human occupants permit intervention in dangerous areas, such as in deep ocean, under ice, in missions to retrieve hazardous objects, or in classified areas. The key element in underwater intervention performed with autonomous vehicles is autonomous manipulation. This is a challenging technology milestone, which refers to the capability of a robot system that performs intervention tasks requiring physical contacts with unstructured environments without continuous human supervision.Today, only few AUVs are equipped with manipulators. SAUVIM (Semi Autonomous Underwater Vehicle for Intervention Mission, University of Hawaii) is one of the first underwater vehicle capable of autonomous manipulation.This paper presents the solutions chosen within the development of the system in order to address the problems intrinsic to autonomous underwater manipulation. In the proposed approach, the most noticeable aspect is the increase in the level of information transferred between the system and the human supervisor.We describe one of the first trials of autonomous intervention performed by SAUVIM in the oceanic environment. To the best knowledge of the authors, no sea trials in underwater autonomous manipulation have been presented in the literature. The presented operation is an underwater recovery mission, which consists in a sequence of autonomous tasks finalized to search for the target and to securely hook a cable to it in order to bring the target to the surface.     

Design and implementation of time efficient trajectories for autonomous underwater vehicles

This paper discusses control strategies adapted for practical implementation and efficient motion of autonomous underwater vehicles (AUVs). For AUVs we would like efficiency in both the measured time and the energy consumption, the mission dictating the weight to put on each of these cost. As a first approach to this problem, we focus in this paper on time minimization. Based on the structure of the time optimal trajectories and of the pure motions, we develop an algorithm to design time efficient trajectories corresponding to piecewise constant thrust arcs with few actuator switchings. We do that by solving a new optimization problem where the unknowns are the time period between two actuator switchings as well as the values of the constant thrust arcs. We apply a direct method to compute the solutions numerically. With our algorithm, we gain considerable computational time. Moreover, with as few as three actuator switchings, the duration of our trajectories is within 10% of the optimal trajectories. Since our control strategies have a simple structure they can be implemented on a test-bed vehicle. For the experiments displayed in this paper we use a spherical underwater vehicle which exhibits with almost no preference of direction or orientation for movement; this gives us a very controllable and versatile vehicle.     

An on-line adaptation method in a neural network based controlsystem for AUVs

A neural network based control system “Self-Organizing Neural-Net-Controller System: SONCS” has been developed as an adaptive control system for Autonomous Underwater Vehicles (AUVs). In this paper, an on-line adaptation method “Imaginary Training” is proposed to improve the time-consuming adaptation process of the original SONCS. The Imaginary Training can be realized by a parallel structure which enables the SONCS to adjust the controller network independently of actual operation of the controlled object. The SONCS is divided into two separate parts: the Real-World Part where the controlled object is operated according to the objective, and the Imaginary-World Part where the Imaginary Training is carried out. In order to adjust the controller network by the Imaginary Training, it is necessary to introduce a forward model network which can generate simulated state variables without involving actual data. A neural network “Identification Network” which has a specific structure to simulate the behavior of dynamical systems is proposed as the forward model network. The effectiveness of the Imaginary Training is demonstrated by applying to the heading keeping control of an AUV “Twin-Burger”. It is shown that the SONCS adjusts the controller network-through on-line processes in parallel with the actual operation     

全姿态水下移动平台模式切换姿态控制研究

为了适应复杂海洋环境中多样性的观探测任务需求,本文提出了一种融合Argo浮标、水下滑翔机（Glider）和自治式水下机器人（Autonomous Underwater Vehicle,AUV） 3种工作模式的全姿态水下移动平台（All-attitude Multimode Underwater Vehicle,AMUV）。首先,基于3种水下移动平台的工作原理,建立了AMUV的六自由度动力学模型;然后,针对动力学模型中的非线性耦合特性及模式切换过程中的驱动位形变化等问题,基于比例、积分、微分控制器（Proportional Integral Derivative,PID）与模糊控制概念,设计了不依赖于数学模型的自适应模糊PID姿态控制器,实现了AMUV多模式切换过程中的姿态控制;最后,开展多模式切换控制仿真实验,将自适应模糊PID控制器与传统PID控制器仿真结果进行对比,并设计了全模式任务工况,仿真结果表明,本文提出的控制器能够精确和稳定地控制AMUV进行多种工作模式的相互切换。     

一种智能水下机器人进行大范围海洋环境监测的方案与实验

采用智能水下机器人进行海洋环境的立体监测具有监测范围广,自主性强的特点。本文在探讨世界各国采用智能水下机器人进行海洋环境监测的情况的基础上,介绍了自主研发的智能水下机器人海洋大范围环境数据的自主采集系统,其主要优点是：相对于其他机器人,可实现“大范围” 海洋环境数据的采集;相对于固定式浮标,可实现海洋环境数据的“自主”采集。并给出了自主采集流程和软件分层递阶体系结构。在真实海域中,采用智能水下机器人,进行了国内首次大范围环境数据采集实航实验。实验结果表明采用智能水下机器人进行海洋环境的立体监测是切实可行的。