A Robust Model Predictive Control Approach for Autonomous Underwater Vehicles Operating in a Constrained workspace

This paper presents a novel Nonlinear Model Predictive Control (NMPC) scheme for underwater robotic vehicles operating in a constrained workspace including static obstacles. The purpose of the controller is to guide the vehicle towards specific way points. Various limitations such as: obstacles, workspace boundary, thruster saturation and predefined desired upper bound of the vehicle velocity are captured as state and input constraints and are guaranteed during the control design. The proposed scheme incorporates the full dynamics of the vehicle in which the ocean currents are also involved. Hence, the control inputs calculated by the proposed scheme are formulated in a way that the vehicle will exploit the ocean currents, when these are in favor of the way-point tracking mission which results in reduced energy consumption by the thrusters. The performance of the proposed control strategy is experimentally verified using a $4$ Degrees of Freedom (DoF) underwater robotic vehicle inside a constrained test tank with obstacles.Comment: IEEE International Conference on Robotics and Automation (ICRA-2018), Accepte

A Mixed-Initiative Formation Control Strategy for Multiple Quadrotors

In this paper, we present a mixed-initiative motion control strategy for multiple quadrotor aerial vehicles. The proposed approach incorporates formation specifications and motion-planning commands as well as inputs by a human operator. More specifically, we consider a leader–follower aerial robotic system, which autonomously attains a specific geometrical formation, by regulating the distances among neighboring agents while avoiding inter-robot collisions. The desired formation is realized by a decentralized prescribed performance control strategy, resulting in a low computational complexity implementation with guaranteed robustness and accurate formation establishment. The multi-robot system is safely guided towards goal configurations, by employing a properly defined navigation function that provides appropriate motion commands to the leading vehicle, which is the only one that has knowledge of the workspace and the goal configurations. Additionally, the overall framework incorporates human commands that dictate the motion of the leader via a teleoperation interface. The resulting mixed-initiative control system has analytically guaranteed stability and convergence properties. A realistic simulation study, considering a team of five quadrotors operating in a cluttered environment, was carried out to demonstrate the performance of the proposed strategy

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 semiautonomous 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

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 semiautonomous 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 state and parameter estimation of an under-actuated underwater vehicle using a modified dual unscented kalman filter

This paper presents a novel modification of the Dual Unscented Kalman Filter (DUKF) for the on-line concurrent state and parameter estimation. The developed algorithm is successfully applied to an under-actuated underwater vehicle. Like in the case of conventional DUKF the proposed algorithm demonstrates quick convergence of the parameter vector. In addition, experimental results indicate an increased performance when the proposed methodology is utilized. The applicability and performance of the proposed algorithm is experimentally verified by combining the proposed DUKF with a non-linear controller on a modified Videoray ROV in a test tank. The on-line estimation of the vehicle states and dynamic parameters is achieved by fusing data from a Laser Vision System (LVS) and an Inertial Measurement Unit (IMU)

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 system's 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