George C. Karras

Localization of an underwater vehicle using an IMU and a laser-based vision system

This paper describes the development of a position tracking system designed for a remotely operated vehicle (ROV). The sensor package consists of an inertial measurement unit (IMU) and a laser-based vision system (LVS). The LVS consists of two undewater laser pointers and a single CCD camera mounted on the ROV. The LVS fuses data deriving from the projection of the laser pointers on the image plane while it tracks a target at the same plane using computer vision algorithms. The LVS calculates the position vector of the vehicle at a low frequency, with respect to the center of the tracked object. The IMU measures the accelerations and angular velocities of the vehicle at a high frequency. The fusion of the two sensors is based on a multisensor Kalman filter where the measured acceleration and angular velocity of the IMU is fed directly to the filter. The result is the calculation of the position vector at a high frequency, which can be used for a smooth closed loop steering control of the vehicle. The integration of the system was proved successful through an extensive experimental procedure.

Target-referenced Localization of an Underwater Vehicle using a Laser-based Vision System

This paper describes the development of an image-based position tracking system designed for a Remotely Operated Vehicle (ROV). The sensor package consists of two underwater laser pointers and a single CCD camera mounted on the ROV. The proposed system fuses data deriving from the projection of the laser pointers on the image plane and computer vision object tracking algorithms. The results deriving from the data fusion compose the position vector of the vehicle, with respect to the center of the tracked object. This position vector can be used for a closed loop steering control of the vehicle. The integration of the system was proved successful through the experimental procedure. The effective working range of the system is 40-150 cm, at 17 Hz refresh rate and 6% of absolute error. The working range is limited due to the existing hardware. However the implemented software is versatile to future hardware upgrades

Towards semi-autonomous operation of under-actuated underwater vehicles: sensor fusion, on-line identification and visual servo control

In this paper we propose a framework for semi-autonomous operation of an under-actuated underwater vehicle. The contributions of this paper are twofold: The first contribution is a visual servoing control scheme that is designed to provide a human operator the capability to steer the vehicle without loosing the target from the vision system’s field of view. It is shown that the under-actuated degree of freedom is input-to-state stable (ISS) and a shaping of the user input with stability guarantees is implemented. The resulting control scheme has formally guaranteed stability and convergence properties. The second contribution is an asynchronous Modified Dual Unscented Kalman Filter (MDUKF) for the on-line state and parameter estimation of the vehicle by fusing data from a Laser Vision System (LVS) and an Inertial Measurement Unit (IMU). The MDUKF has been developed in order to experimentally verify the performance of the proposed visual servoing control scheme.Experimental results of the visual servoing control scheme integrated with the asynchronous MDUKF indicate the feasibility and applicability of the proposed control scheme. Experiments have been carried out on a small under-actuated Remotely Operated Vehicle (ROV) in a test tank.

On-line identification of autonomous underwater vehicles through global derivative-free optimization

We describe the design and implementation of an on-line identification scheme for Autonomous Underwater Vehicles (AUVs). The proposed method estimates the dynamic parameters of the vehicle based on a global derivative-free optimization algorithm. It is not sensitive to initial conditions, unlike other on-line identification schemes, and does not depend on the differentiability of the model with respect to the parameters. The identification scheme consists of three distinct modules: a) System Excitation, b) Metric Calculator and c) Optimization Algorithm. The System Excitation module sends excitation inputs to the vehicle. The Optimization Algorithm module calculates a candidate parameter vector, which is fed to the Metric Calculator module. The Metric Calculator module evaluates the candidate parameter vector, using a metric based on the residual of the actual and the predicted commands. The predicted commands are calculated utilizing the candidate parameter vector and the vehicle state vector, which is available via a complete navigation module. Then, the metric is directly fed back to the Optimization Algorithm module, and it is used to correct the estimated parameter vector. The procedure continues iteratively until the convergence properties are met. The proposed method is generic, demonstrates quick convergence and does not require a linear formulation of the model with respect to the parameter vector. The applicability and performance of the proposed algorithm is experimentally verified using the AUV Girona 500.

Toward persistent autonomous intervention in a subsea panel

Intervention autonomous underwater vehicles (I-AUVs) have the potential to open new avenues for the maintenance and monitoring of offshore subsea facilities in a cost-effective way. However, this requires challenging intervention operations to be carried out persistently, thus minimizing human supervision and ensuring a reliable vehicle behaviour under unexpected perturbances and failures. This paper describes a system to perform autonomous intervention—in particular valve-turning—using the concept of persistent autonomy. To achieve this goal, we build a framework that integrates different disciplines, involving mechatronics, localization, control, machine learning and planning techniques, bearing in mind robustness in the implementation of all of them. We present experiments in a water tank, conducted with Girona 500 I-AUV in the context of a multiple intervention mission. Results show how the vehicle sets several valve panel configurations throughout the experiment while handling different errors, either spontaneous or induced. Finally, we report the insights gained from our experience and we discuss the main aspects that must be matured and refined in order to promote the future development of intervention autonomous vehicles that can operate, persistently, in subsea facilities.

A self-triggered visual servoing model predictive control scheme for under-actuated underwater robotic vehicles

This paper presents a novel Vision-based Nonlinear Model Predictive Control (NMPC) scheme for an under-actuated underwater robotic vehicle. In this scheme, the control loop does not close periodically, but instead a self-triggering framework decides when to provide the next control update. Between two consecutive triggering instants, the control sequence computed by the NMPC is applied to the system in an open-loop fashion, i.e, no state measurements are required during that period. This results to a significant smaller number of requested measurements from the vision system, as well as less frequent computations of the control law, reducing in that way the processing time and the energy consumption. The image constraints (i.e preserving the target inside the cameras field of view), the external disturbances induced by currents and waves, as well as the vehicles kinematic constraints due to under-actuation, are being considered during the control design. The closed-loop system has analytically guaranteed stability and convergence properties, while the performance of the proposed control scheme is experimentally verified using a small under-actuated underwater vehicle in a test tank.

Towards the stabilization of an underactuated underwater vehicle in the presence of unknown disturbances

In this paper a dynamic model based control scheme is proposed for the stabilization of an underactuated underwater vehicle, in the presence of slowly varying, unknown disturbances. An unscented Kalman filter (UKF), based on the vehicles dynamic model, is applied for the sensor fusion process to provide an estimation of the full state vector of the system. External disturbances and unmodeled phenomena are included in the dynamic model as zero-mean Gaussian white noise processes. The estimation of the state vector is used as feedback for the proposed control scheme, which stabilizes the vehicle to the desired position and orientation. The proposed methodology is experimentally implemented using a 3-DOF underactuated vehicle. The efficiency of the methodology under various environmental conditions is demonstrated by simulation results. The overall system can be used for inspection tasks of sea platforms, e.g. during the inspection of a ship hull for possible damage tracking.

Visual servo control of an underwater vehicle using a Laser Vision System

This paper describes a position-based visual servo control scheme designed for an underwater vehicle. The methodology proposes a path planning technique, which guarantees that a flat target is kept in the camera optical field, while the vehicle avoids collision with the surface the target lays on. The vehicle pose (position and orientation) with respect to the target is obtained using a laser vision system (LVS). The LVS projects two laser dots in the image plane while it tracks the target using computer vision algorithms. The position of each laser dot in the image plane is directly related to the distance between the vehicle and the surface the target is located. The path planning strategy is based on the artificial potential field method (APF). The attractive part of the APF is responsible for minimizing the error between the current vehicle position and the desired. The repulsive part of the APF restricts the target inside the camera optical field while keeps away the laser dots from image regions related to small distances between the vehicle and the surface the target is located. The steering control of the vehicle is achieved by feeding the computed points of the path planning into a Cartesian kinematic controller, which was slightly modified for the needs of the methodology. The overall efficiency of the system, was proved through an extensive experimental procedure, using a small remotely operated vehicle (ROV) in a test tank.

A robust visual servo control scheme with prescribed performance for an autonomous underwater vehicle

This paper describes the design and implementation of a visual servo control scheme for an Autonomous Underwater Vehicle (AUV). The purpose of the control scheme is to navigate and stabilize the vehicle towards a visual target. The controller does not utilize the vehicles dynamic model parameters and guarantees prescribed transient and steady state performance despite the presence of external disturbances representing ocean currents and waves. The proposed control scheme is of low complexity and can be easily integrated to an embedded control platform of an Autonomous Underwater Vehicle (AUV) with limited power and computational resources. Moreover, through the appropriate selection of certain performance functions, the proposed scheme guarantees that the target lies inside the onboard cameras field of view for all time. The resulting control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using the Girona 500 AUV.

A visual-servoing scheme for semi-autonomous operation of an underwater robotic vehicle using an IMU and a Laser Vision System

This paper presents a visual servoing control scheme that is applied to an underwater robotic vehicle. The objective of the proposed control methodology is to provide a human operator the capability to move the vehicle without loosing the target from the vision systems field of view. On-line estimation of the vehicle states is achieved by fusing data from a Laser Vision System (LVS) and an Inertial Measurement Unit (IMU) using an asynchronous Unscented Kalman Filter (UKF). A controller designed at the kinematic level, is backstepped into the dynamics of the system, maintaining its analytical stability guarantees. It is shown that the under-actuated degree of freedom is input-to-state stable and an energy based shaping of the user input with stability guarantees is implemented. The resulting control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using a small Remotely Operated Vehicle (ROV) in a test tank.