The thruster is the crucial factor of an underwater vehicle system, because it is the lowest layer in the control loop of the system. However, an accurate and practical thruster model has not been utilized yet. In this paper, we propose an accurate and practical thrust modeling for underwater vehicles which considers the effects of ambient flow velocity. In this model, the axial flow velocity of the thruster, which is non-measurable, is represented by ambient flow velocity and propeller shaft velocity. Hence, contrary to previous models, the proposed model is practical since it uses only measurable states. Next, the whole thrust map is divided into three states according to the state of ambient flow and propeller shaft velocity, and one of the borders of the states is defined as Critical Advance Ratio (CAR). This classification explains the physical phenomenon of conventional experimental thrust maps. The proposed model is evaluated by comparing experimental data with numerical model simulation data, and it accurately covers overall flow conditions within ± 2N force error. The comparison results show that the new model’s matching performance is significantly better than conventional models’.

To improve the maneuverability of underwater vehicles, accurate thrust model is required. However, its modeling is not only difficult but also inaccurate, because it has highly nonlinear dynamics and many parameters which cannot be modeled exactly. Generally, a propeller's steady-state axial thrust is proportional to the signed square of propeller shaft velocity under bollard pull condition. And, it is reported that the velocity of the ambient flow of the thruster affects the thrust force largely. From these facts, we can easily make a reasonable inference that the thrust force may also be affected by the thruster spouting angle with respect to the vehicle moving direction. However, the effect of this non-parallel ambient flow is not well understood in the previous works. Hence, in this paper, a new parameter, what we call incoming angle, is introduced and its effect on thrust force is shown. Incoming angle is defined as the angle between the direction of thrust and that of vehicle velocity. The effect of this angle can be dominant during an underwater vehicle changes its direction, or an omni-directional vehicle carries out its task. The effect cannot be compensated by including the only parallel component of vehicle velocity into previous models. In this paper, based on the three axial flow states classification model, the effect of the incoming angle of ambient flow is analyzed, and critical incoming angle (CIA) is also defined to describe the thrust force states according to incoming angle. In order to characterize the effect of non-parallel ambient flow, the proposed model is evaluated by comparing experimental data with numerical model simulation data, and it accurately covers overall flow conditions within /spl plusmn/2N force error.

The control issue of underwater robots is very challenging due to the nonlinearity, time variance, unpredictable external disturbances, such as the sea current fluctuation, and the difficulty in accurately modeling the hydrodynamic effect. Conventional linear controllers may fail in satisfying performance requirements, especially when changes in the system and environment occur during the operation since it is almost impossible to manually retune the control parameters in water. Therefore, it is highly desirable to have an underwater robot controller capable of self-adjusting control parameters when the overall performance degrades. This paper presents the theory and experimental work of the adaptive plus disturbance observer (ADOB) controller for underwater robots, which is robust with respect to external disturbance and uncertainties in the system. This control scheme consists of disturbance observer (DOB) as the inner-loop controller and a nonregressor based adaptive controller as the outer-loop controller. The effectiveness of the ADOB was experimentally investigated by implementing three controllers: PID, PID plus DOB, and ADOB on an autonomous underwater robot, ODIN III.

There are several kinds of singularity in controlling robotic manipulator. Among those, the kinematic and algorithmic singularity avoidance have been investigated intensively. What seems to be lacking, however, is excessive performance reduction in non-singular region and difficulty in performance tuning. In this paper, we develop a new method to solve kinematic and algorithmic singularities using task reconstruction approach. This new method drives the manipulator singularity-free path, and simultaneously guarantees task performance. And it can be easily extended singularity avoidance of multiple tasks case in task priority based method of redundant manipulators. The advantage and performance of the proposed method is validated by simulation works.

This paper presents the theory and experimental work of the adaptive plus disturbance observer (DOB) controller on underwater robot. This control scheme consists of DOB as an inner-loop compensator and a non-regressor based adaptive controller as an outer-loop controller. It is regressor-free, and is robust with respect to external disturbance as well as the variation of the nominal model used to construct DOB. The performance and characteristics of this controller are shown through comparison tests among four different controllers: PID controller, PID plus DOB controller, adaptive controller and adaptive plus DOB controller.

This paper identifies the key issues in the empirical implementation of redundant marine thrusters for two different autonomous underwater vehicles of different designs, so as to maximize the effectiveness for fine motion control. A crucial factor in the development of a precision control scheme is the thorough understanding of the system geometry. Other critical elements include the contribution of the thruster amplifier gains to the steady state response of the thrusters, and the influence of the power source on system performance. These findings are proven through experimental analysis and verification.

During the last decade, the first autonomous underwater robot of the Autonomous Systems Laboratory of the University of Hawaii, ODIN (Omni-Directional Intelligent Navigator) built in 1991 has produced a lot of valuable results in development and control methods [(S.K. Choi et al., July 1994), (Choi, S.K. et al., March 1995), (S.K. Choi et al., April 1996), (Yang, K.C. et al., September 1999), (J. Yuh et al., 2000)]. Recently, ODIN was born again in the 3rd generation with unique features under recent technologies such as abundant system resources owing to a PC104+, new vehicle system software architectures with an object-oriented concept and its implementation, a graphical user interface and an independent algorithm module using a dynamic linking library (DLL) based on the Windows operating system. These give us an ideal environment for developing various algorithms which are needed for developing an advanced underwater robotic vehicle. This paper describes details of the development of ODIN-III and presents initial experimental results for fine motion control.

A unified adaptive force control approach for underwater vehicle manipulator systems (UVMS) is proposed in this paper. First, a direct adaptive impedance control scheme is introduced. This controller is further incorporated into the unified force control strategy, which combines adaptive impedance control with hybrid position/force control by means of fuzzy switching to perform autonomous underwater manipulation. This approach combines the advantages of impedance control with hybrid control without knowing the accurate dynamic model of the system and has the potential to be effective in underwater environment. Extensive computer simulations are performed to verify the efficacy of the proposed control scheme based on a UVMS model with 6 DOF autonomous underwater vehicle and a 3 DOF robot arm that is mounted on the vehicle.

This paper considers thruster dead zones and saturation limits in fine motion control of underwater robots. Thruster dead zones and saturation limits are major nonlinear elements that make fine motion control of underwater robots difficult. If the vehicle is designed with a redundant thruster configuration, the dead zone and the nonlinear region around it can be completely avoided by implementing a null motion solution for the command input of the vehicle. This solution is derived primarily from the hardware geometry, and is actually realized before the application of the motion control algorithm. The result is an improvement in system performance regardless of the implemented controller type. This paper describes the mathematical derivation of a null motion solution, the minimum energy solution, and a hybrid of the two. The approach is then illustrated through simulation, and followed by empirical application to an underwater vehicle that was developed at the Autonomous Systems Laboratory of the Mechanical Engineering Department of the University of Hawai'i at Manoa.

This paper describes the development of a laser-based ranging device, as part of a sensor work package that is to be integrated into an autonomous underwater vehicle. The objective of the laser ranging component includes robust operation in turbid ocean environments from 1 to 10 meters with a balance of accuracy and speed. A calibration technique that utilizes a spline interpolation is employed as an accurate, yet simple alternative to conventional triangulation methods. The resulting configuration is capable of range detection within 2% absolute error, at a 10 Hz refresh rate, out to 4 meters in turbid ocean conditions, and to greater than 10 meters in air. The maximum underwater range of the system is currently limited by the existing hardware, whereas the software is ready for any future system upgrades.

Underwater vehicle-manipulator (UVM) system becomes popular for various applications although it has complexity in control and analysis when compared to the vehicle itself. To resolve the complexity, firstly, thorough kinematic and dynamic analysis on the whole UVM system is performed. Then, the whole dynamics is divided into several meaningful terms. A robust coordinated motion control algorithm for autonomous UVM systems is considered next based on the analysis of divided dynamics. Since the vehicle has relatively smaller bandwidth than manipulators, active damping control with two-time scale approach for the manipulator is proposed. Using this algorithm, the manipulator controller can cover the vehicle state. The performance of the proposed algorithm is verified by computer simulations.

This paper presents task description language (TDL) for underwater robots. General primitives of TDL were designed by considering common primitives of conventional computer languages, and underwater robot-specific command primitives were designed by modifying the turtle graphic commands in Lego. An alias and procedure commands make the TDL intuitive and modular. Concurrent processing and sensor-based event handling commands are also available with TDL. An example of survey task with lawnmower pattern shows feasibility and easiness of TDL.

In this paper we describe an online solution for avoiding the occurrence of both algorithmic and kinematic singularities in task-priority based kinematic controllers of robotic manipulators. The proposed approach uses a secondary task correction and a successive task projection in order to maintain the measure of manipulability of the correspondent space augmentation approach over a minimum value. It shows a gain in performance and a better task error especially when working in proximity of singular configurations. It is particularly suitable for autonomous systems where an offline trajectory control scheme is often not applicable.

This paper presents a six degree-of-freedom nonlinear H-/sub /spl infin// controller for autonomous underwater vehicles. The controller makes use of Euler parameters, or unit quaternion, which have been used extensively with spacecraft and aircraft as opposed to the more popular Euler angle representation. Euler angles suffer as with other 3-parameter representations from singular points. One of the main challenges associated with designing an underwater control is the unknowns of the model. The nonlinear H-/sub /spl infin// controller attempts to address this problem such that the closed loop system has L/sub 2/ - gain /spl les/ /spl gamma/ from torque disturbances to a penalty variable for a fixed positive constant /spl gamma/.

This paper presents an adaptive DOB (disturbance observer) controller for underwater robots, which consists of DOB as an inner-loop controller and a non-regressor based adaptive controller as an outer-loop controller. This control scheme does not require any parametric information, and is robust with respect to external disturbance as well as the variation of the nominal model used to construct DOB. Results of computer simulation using a hydrodynamic model of an underwater robot, ODIN, show the effectiveness of the adaptive DOB controller compared to PID, PID DOB, and adaptive controller. Experimental investigation of the presented controller is in progress on ODIN and results are also presented at IROS 2003.

In autonomous system, it is important to establish a control scheme that works with stability even near singularity configurations. We describe an online trajectory control scheme that uses the manipulability measure as a distance criteria to avoid manipulator singularities. The proposed approach consists in a method for limiting the minimum value of the distance criteria. The performance is simply affected by the choice of the lower limit. Based on a real-time evaluation of the measure of manipulability, this method does not require a preliminary knowledge of the singular configurations. The proposed algorithm is validated by experimental results.

In this paper, we describe an online trajectory control scheme for robotic manipulators with dynamic change of the task priority. First, given tasks are reconstructed using a geometric projection on the given index function. Then, the reconstructed tasks are analyzed in the framework of task priority based method. From this analysis, the proposed algorithm is shown to have the property that the task priority is assigned dynamically. Using this algorithm, we easily set the criteria of changing task priority and estimate the performance of the given index function. Considering the measure of manipulability as a index function, the approach is suitable for avoiding kinematic singularities for autonomous operation of, for example, an underwater robot. The result shows a good performance near the singular configurations, as shown by simulation results.

The distributed virtual environment collaborative simulator is used for testing of unmanned underwater vehicles (UUVs) where both real and simulated vehicles can interact and cooperate in a hybrid, synthetic, virtual environment. This virtual reality system can be used to determine: 1) the optimal performance and criteria for the cooperating vehicles and its relative application; 2) the determination of the advantages and disadvantages of collaborative application tasks between multiple UUVs; and 3) the optimal communication links between the cooperating vehicles and its remote control stations. It also utilizes a virtual reality projection and polarized eye-wear systems, which immerses the user for optimal visualization.

Presents a neuro-fuzzy controller for autonomous underwater vehicles (AUVs) of which the dynamics are highly nonlinear, coupled, and time-varying. Modified fuzzy membership function-based neural networks (FMFNN) are used to combine advantages of fuzzy logics and neural networks, such as inference capability and adoption of human operators' fuzzy logics, and universal learning capability with neural networks. With initial fuzzy rules given by a human operator or automatically generated by a controller, the AUV can learn appropriate control parameters without human intervention, taking into account the differences in sensor characteristics in different environments. Internal learning loops and simplified derivatives of unknown systems are used for fast and simple converging algorithms. Compared to other control methods, the proposed FMFNN control algorithm never requires any information on systems, off-line learning procedures, or human intervention to adjust parameters. To show the validity of the proposed algorithm, computer simulation results are presented.

The control of autonomous underwater robotic vehicles includes many challenges. The highly nonlinear, time-varying dynamic behavior of the robot continually changes the parameters of the system model. The uncertainties in hydrodynamic coefficient make system identification difficult. Disturbances acting on the robot, such as ocean currents, continually affect the system. The paper presents a non-regressor based adaptive control scheme for use on an underwater robotic vehicle. The control scheme is non-model based and, thus, does not require any knowledge of the system. The adaptive control algorithm estimates the control gains defined by the combinations of the bounded constants of system parameter matrices. The performance of the algorithm was evaluated through simulation. Additionally, experimental data using the ODIN vehicle was analyzed.

Hydrodynamics of the manipulator mounted on the vehicle are poorly known and time-varying. Furthermore, its motion is disturbed by the vehicle motion and current. This paper presents a non-regressor based adaptive control scheme with a disturbance observer for tracking the trajectory of underwater mobile platform-mounted manipulators. The presented control system does not require any information about the system. The disturbance observer regards the difference between the actual output and the output of the nominal model as an equivalent disturbance applied to the nominal model. The adaptive control law then estimates control gains defined by the combinations of the bounded constants of parameter matrices of the nominal model with disturbance error. The performance of the proposed control scheme was evaluated by a computer simulation with a two-link planar manipulator mounted on a one 1-DOF mobile platform. The effect of hydrodynamic forces acting on the manipulator was also considered.

Presents a systematic approach for developing a concise self-adaptive neuro-fuzzy inference system (SANFIS) with a fast hybrid parameter learning algorithm for online learning the control knowledge for autonomous underwater vehicles (AUV). The multi-layered structure of SANFIS incorporates fuzzy basis functions for better function approximations. Based on the need of different applications, we investigate three SANFIS structures with three different types of fuzzy IF-THEN-rule-based models and cast the rule formation problem as a clustering problem. A recursive least squares algorithm and a modified Levenberg-Marquardt algorithm with limited memory are exploited to accelerate the learning process. Thus, incorporating an online clustering technique, a fast hybrid learning procedure and rule examination, the SANFIS is capable of self-organizing and self-adapting its internal structure for learning the required control knowledge for an AUV to follow desired trajectories. Computer simulations for modeling a control system for an AUV have been conducted to validate the effectiveness of the proposed SANFIS.

As the ocean attracts great attention on environmental issues and resources as well as scientific and military tasks, the need for and use of underwater robotic systems has become more apparent. Underwater robotics represents a fast growing research area and promising industry as advanced technologies in various subsystems develop and potential application areas are explored. Great efforts have been made in developing autonomous underwater vehicles (AUVs) to overcome challenging scientific and engineering problems caused by the unstructured and hazardous ocean environment. With the development of new materials, advanced computing and sensory technology, as well as theoretical advancements, R & D activities in the AUV community have increased. This paper describes current state-of-the art in the area of underwater robotics focusing on some key subsystems.

This paper describes a distributed virtual environment collaborative simulator (DVECS) for testing of unmanned underwater vehicles (UUV) where both real and simulated vehicles-remotely operated vehicles (ROV) and autonomous underwater vehicles (AUV)-can interact and cooperate in a hybrid, synthetic, virtual environment containing various types of real and simulated vehicles, obstacles, conditions and disturbances. This virtual system can be used to determine (1) the optimal performance criteria for the vehicles and its application, (2) the advantages and disadvantages of collaborative application tasks between multiple UUV, and (3) the optimal communication links between remote locations. The current DVECS is being tested between the University of Hawaii in Honolulu and the University of Tokyo in Tokyo as a joint, international research program. It also utilizes virtual reality projection system, which immerses the user for optimal visualization.

This paper surveys control architectures proposed in the literature and describes a control architecture that is being developed for a semi-autonomous underwater vehicle for intervention missions (SAUVIM) at the University of Hawaii. Conceived as hybrid, this architecture has been organized in three layers: planning, control and execution. The mission is planned with a sequence of subgoals. Each subgoal has a related task supervisor responsible for arranging a set of pre-programmed task modules in order to achieve the subgoal. Task modules are the key concept of the architecture. They are the main building blocks and can be dynamically re-arranged by the task supervisor. In our architecture, deliberation takes place at the planning layer while reaction is dealt through the parallel execution of the task modules. Hence, the system presents both a hierarchical and an heterarchical decomposition, being able to show a predictable response while keeping rapid reactivity to the dynamic environment.

Controlling autonomous underwater vehicles (AUVs) in an uncertain and unstructured environment presents many challenging control problems. Model-based control strategies have been used with limited success. The paper presents an online self-organizing neuro-fuzzy control that serves as a better alternative control scheme in controlling AUVs. The proposed self-organizing neuro-fuzzy controller is a six-layer feedforward neural network that is capable of self-constructing and self-restructuring its internal node connectivity and learning the parameters of each node based on incoming training data. Computer simulations have been conducted to validate the performance of the proposed neuro-fuzzy controller and an experimental verification has been scheduled to verify if on ODIN, an autonomous underwater vehicle developed at the University of Hawaii.

The control of underwater robots presents a number of unique and formidable challenges. Underwater robot dynamics are highly nonlinear, coupled, and time-varying, and subject to hydrodynamic uncertainties and external disturbances such as current. Unlike land mobile robots, underwater robots cannot use GPS. Most popular underwater positioning sensors are sonar-based, such as long-base line. However, autonomous processing of sonar measurements is plagued by noise, drop-outs, missed detection, false reading, poor resolution, etc. The paper presents a new adaptive control of underwater robots with sonar-based position measurements. Experimental results show robustness of the control system in the presence of unmodelled dynamics and various noise.

The control of underwater vehicle-manipulator system (UVMS) is a challenging task because the dynamics of such a system is highly nonlinear, coupled, time-varying, and subject to hydrodynamic uncertainties and external disturbances. The paper presents a non-regressor based adaptive control for UVMS where centralized and coordinated trajectory planning is followed by decentralized control-one for the slower subsystem (vehicle) and other for the faster subsystem (manipulator). To avoid joint limits, kinematic redundancy of the system is resolved using the weighted pseudoinverse of the Jacobian matrix. The results from computer simulations demonstrate the effectiveness of this proposed method.

Presents an online self-adaptive neuro-fuzzy control that serves as a better alternative control scheme in controlling autonomous underwater vehicles (AUVs) in an uncertain and unstructured environment. The proposed self-adaptive neuro-fuzzy controller is a five-layer feedforward neural network that implements fuzzy basis function (FBF) expansions and is capable of self-constructing and self-restructuring its internal node connectivity and learning the parameters of each node based on incoming training data. Computer simulations have been conducted to validate the performance of the proposed neuro-fuzzy controller and an experimental verification has been scheduled to verify it on ODIN, an autonomous underwater vehicle developed at the University of Hawaii.

Describes the design and implementation of a fault tolerant system for the 6 degrees-of-freedom ODIN autonomous underwater vehicle (AUV). Fault tolerant system design can be divided into three areas: fault detection, fault isolation, and fault accommodation. This paper summarizes previous studies in fault tolerant system design and presents a fault tolerant system design for thruster (actuator) and sensor failure in AUVs. The presented system was implemented in the actual vehicle, ODIN, and its effectiveness was evaluated with experimental results.

This paper presents a model-free adaptive control scheme for the trajectory tracking of robot manipulators. The presented adaptive law, unlike most existing ones, estimates parameters defined by combinations of unknown bounded constants of system parameter matrices rather than each unknown parameter of the system. It results in an adaptive controller that is structurally simple and computationally efficient. The stability analysis by using Lyapunov method is presented. Results of computer simulation show effectiveness of the proposed controller for slow and fast desired motions; different initial positions of joints from the desired trajectory; measurement noise; and a sudden payload change.

Many underwater robotic vehicles use propeller-type thrusters driven by DC motors. The thruster system is known to be nonlinear and time-varying. Robust control of the thruster system is crucial to navigate and achieve high performance. This paper presents experimental results of five different thruster control systems based on online neural network (NN) control, off-line NN control, fuzzy control, adaptive-learning control and PID control. These control systems were initially designed or trained for the thruster system with no fin. Then each controller was implemented for the thruster system modified with different size fins. The fins changed the system hydrodynamics, especially the drag force. In this paper, each control system is briefly described and its practical advantages and disadvantages are discussed with experimental results.

Underwater robotic vehicles (URVs) have become an important tool for various underwater tasks because of their greater speed, endurance, depth capability and a lower risk factor than human divers. However, most vehicle control system designs are based on simplified vehicle models and often result in poor vehicle performance due to the nonlinear and time-varying vehicle dynamics having parameter uncertainties. This paper describes a new vehicle control system capable of learning and adapting to changes in the vehicle dynamics and parameters. This control system is compared with a conventional linear control system through extensive wet tests. Results show the learning and adapting capabilities of the presented control system.

The authors present a real-time vision-based system for automatic positioning and trajectory following, based on a direct method for 3D motion estimation. The spatio-temporal derivatives of the image function, calculated from time-varying imagery, are used to directly calculate the motion and position of the camera. For demonstration, they have implemented the system on a one-degree-of freedom thruster operating in a laboratory water tank. The estimated position information is communicated to the control system, a PID controller, in order to generate the appropriate signal to correct the thruster system's position. The performance of the vision system is demonstrated in selected experiments by comparing results with the data from an optical encoder position sensor.

This paper is written as a survey paper summarizing the current state-of-the-art and discussing future research directions in the area of underwater robotics. The topics discussed include: dynamics and modelling, intelligent systems, sensors, and control.

With the increased utilization of remotely operated vehicles in subsea applications, the development of autonomous vehicles becomes highly desirable to enhance operator efficiency. The dynamic model of an untethered remotely operated underwater vehicle is presented, and an adaptive control strategy for such vehicles is described. The robustness of the control system with respect to nonlinear dynamic behavior and parameter uncertainties is investigated by computer simulation. The results show that the use of the adaptive control system can provide high performance of the vehicle in the presence of unpredictable changes in the dynamics of the vehicle and its environment.>