A Predictive Control Approach for Cooperative Transportation by Multiple Underwater Vehicle Manipulator Systems

This article addresses the problem of cooperative object transportation for multiple underwater vehicle manipulator systems (UVMSs) in a constrained workspace involving static obstacles. We propose a nonlinear model predictive control (NMPC) approach for a team of UVMSs in order to transport an object while avoiding significant constraints and limitations, such as kinematic and representation singularities, obstacles within the workspace, joint limits, and control input saturation. More precisely, by exploiting the coupled dynamics between the robots and the object and using certain load sharing coefficients, we design a predictive controller for each UVMS in order to cooperatively transport the object within the workspace's feasible region. Moreover, the control scheme adopts load sharing among the UVMSs according to their specific payload capabilities. In addition, the feedback relies on each UVMS's onboard measurements and no explicit data are exchanged online among the robots, thus reducing the required communication bandwidth. Finally, realistic simulation results conducted in the UwSim dynamic simulator running in robot operating system (ROS) environment as well as real-time experiments employing two small UVMSs and demonstrated the effectiveness of the proposed control strategy. © 2022 IEEE

A Robust Predictive Control Approach for Underwater Robotic Vehicles

This article presents a robust nonlinear model predictive control (NMPC) scheme for autonomous navigation of underwater robotic vehicles operating in a constrained workspace including the static obstacles. In particular, the purpose of the controller is to guide the vehicle toward specific way points with guaranteed input and state constraints. Various constraints, such as obstacles, workspace boundaries, predefined upper bounds for the velocity of the robotic vehicle, and thruster saturations, are considered during the control design. Moreover, the proposed control scheme is designed at dynamic level, and it incorporates the full dynamics of the vehicle in which the ocean currents are also involved. Hence, taking the thrusts as the control inputs of the robotic system and formulating them accordingly, the vehicle exploits the ocean current dynamics when these are in favor of the way-point tracking mission, resulting in reduced energy consumption by the thrusters. The robustness of the closed-loop system against parameter uncertainties has been analytically guaranteed with convergence properties. 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 sparse static obstacles. © 1993-2012 IEEE

Cooperative Impedance Control for Multiple Underwater Vehicle Manipulator Systems under Lean Communication

This article addresses the problem of cooperative object transportation for multiple underwater vehicle manipulator systems (UVMSs) in a constrained workspace with static obstacles, where the coordination relies solely on implicit communication arising from the physical interaction of the robots with the commonly grasped object. In this article, we propose a novel distributed leader-follower architecture, where the leading UVMS, which has knowledge of the object's desired trajectory, tries to achieve the desired tracking behavior via an impedance control law, navigating in this way, the overall formation toward the goal configuration while avoiding collisions with the obstacles. On the other hand, the following UVMSs estimate locally the object's desired trajectory via a novel prescribed performance estimation law and implement a similar impedance control law that achieves tracking of the desired trajectory despite the uncertainty and external disturbance in the object and the UVMS dynamics, respectively. The feedback relies on each UVMS's force/torque measurements and no explicit data is exchanged online among the robots, thus reducing the required communication bandwidth and increasing robustness. Moreover, the control scheme adopts load sharing among the UVMSs according to their specific payload capabilities. Finally, various simulation studies clarify the proposed method and verify its efficiency. © 1976-2012 IEEE

Sensor-based motion control of autonomous underwater vehicles, part II: Robust motion control strategies

The first section of this chapter presents an NMPC strategy for underwater robotic vehicles operating under various constraints. The purpose of the controller is to guide the vehicle towards specific way -points. Various constraints such as obstacles, workspace boundaries and control input saturation as well as predefined upper bound of the vehicle velocity (requirements for several underwater tasks such as seabed inspection scenario and mosaicking) are considered during the control design. The proposed scheme incorporates the full dynamics of the vehicle in which the ocean currents are also involved. The controller is designed in order to find the optimal thrusts required for minimizing the way -point tracking error. Moreover, the controlinputs calculated by the proposed approach are formulated in a way that the vehicle will exploit the ocean currents, when they are in favor of the way -point tracking mission, which results in reduced energy consumption by the thrusters. In the second part of this chapter, novel position- and trajectory -tracking control schemes for AUVs are presented. The proposed controllers do not utilize the vehicle’s dynamic model parameters and guarantee prescribed transient and steady-state performance despite the presence of external disturbances and kinematic constraints for the case of underactuated vehicles. Moreover, through the appropriate selection of certain performance functions, the proposed scheme can also guarantee the satisfaction of motion and performance constraints imposed by the desired task. © The Institution of Engineering and Technology 2020

A self-triggered position based visual servoing model predictive control scheme for underwater robotic vehicles

An efficient position based visual sevroing control approach for Autonomous Underwater Vehicles (AUVs) by employing Non-linear Model Predictive Control (N-MPC) is designed and presented in this work. In the proposed scheme, a mechanism is incorporated within the vision-based controller that determines when the Visual Tracking Algorithm (VTA) should be activated and new control inputs should be calculated. More specifically, the control loop does not close periodically, i.e., between two consecutive activations (triggering instants), the control inputs calculated by the N-MPC at the previous triggering time instant are applied to the underwater robot in an open-loop mode. This results in a significantly smaller number of requested measurements from the vision tracking algorithm, as well as less frequent computations of the non-linear predictive control law. This results in a reduction in processing time as well as energy consumption and, therefore, increases the accuracy and autonomy of the Autonomous Underwater Vehicle. The latter is of paramount importance for persistent underwater inspection tasks. Moreover, the Field of View constraints (FoV), control input saturation, the kinematic limitations due to the underactuated degree of freedom in sway direction, and the effect of the model uncertainties as well as external disturbances have been considered during the control design. In addition, the stability and convergence of the closed-loop system has been guaranteed analytically. Finally, the efficiency and performance of the proposed vision-based control framework is demonstrated through a comparative real-time experimental study while using a small underwater vehicle. © 2020 by the authors

Image Based Visual Servoing for Floating Base Mobile Manipulator Systems with Prescribed Performance under Operational Constraints

This paper presents a novel Image-Based Visual Servoing (IBVS) control approach for Floating Base Mobile Manipulator Systems (FBMMSs) that imposes prescribed transient and steady-state response on the image feature coordinate errors while satisfying the visibility constraints that arise owing to the camera’s limited field of view. The proposed control strategy does not incorporate any knowledge on either the FBMMS dynamic model, the exogenous disturbances, or the inevitable camera calibration and depth measurement errors. More specifically, it guarantees: (i) predefined behavior in terms of overshoot, convergence rate, and maximum steady-state error value of the image features and system velocities tracking errors; (ii) satisfaction of camera field of view constraints; (iii) bounded closed-loop control signals, and (iv) reduced design and implementation complexity. Additionally, the performance of the developed scheme is solely determined by certain designer-specified performance functions/parameters, and it is fully decoupled by the control gains selection. The efficiency of the proposed scheme is demonstrated via a realistic simulation study, using an eye-in-hand Underwater Vehicle Manipulator System (UVMS) as a test-bed FBMMS platform. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Sensor-based motion control of autonomous underwater vehicles, part I: Modeling and low-complexity state estimation

The goal of this chapter is to provide the reader with the appropriate analytical methods, in order to derive a simple yet accurate vehicle model, design a state estimation algorithm that could be easily integrated into the embedded system framework of an underwater robotic vehicle and perform a fast online dynamic parameter identification. © The Institution of Engineering and Technology 2020