Underwater Vehicle-manipulator System Research Articles

A coordinated trajectory tracking method with active utilization of drag for underwater vehicle manipulator systems

UVMSs suffer severe drag owing to the manipulator while tracking a trajectory at a considerable speed. Drag, considered a negative force by other researchers, can be utilized for UVMS trajectory tracking. To this end, a novel coordinated trajectory tracking method for UVMS is proposed to actively utilize drag, which consists of three parts: a trajectory planner is developed to plan the desired velocity of the vehicle for trajectory tracking; also, a motion planner is designed to schedule the movement of the manipulator to actively utilize drag and the generating turning torque on the vehicle, making the vehicle approach the desired angular velocity; additionally, a computed torque controller (CTC) is developed to control the linear velocity and joint positions and its stability is demonstrated. Different simulations are performed, the results show that drag can be actively utilized for trajectory tracking, and the combination of the proposed method with other actuators can improve the maneuverability and tracking performance of the system. Pool experiments are conducted to verify the effectiveness of the proposed method in tracking yaw angles.

Dynamic robust path-following control of UVMS subject to manipulator disturbance

This study addresses path-following control issue of underwater vehicle-manipulator systems (UVMS) in the presence of manipulator disturbances. It considers disturbance forces arising from manipulator position changes, including restorative forces and coupling forces, to achieve precise UVMS path-following control. By analysing the variations in the center of mass and buoyant center of manipulator links during motion, the corresponding restorative torque is deduced, and disturbance forces are calculated. Additionally, joint coupling forces generated during manipulator startup, braking, and rotation are taken into account. These aspects collectively yield the expression for disturbance forces. Subsequently, adaptive sliding mode control are employed to achieve accurate control of the UVMS system. This study effectively addresses the issue of UVMS body control under manipulator disturbances. By analysing the motion of manipulator links, the disturbance forces exerted by the manipulator on the UVMS are identified, offering insights for similar control challenges arising from manipulator disturbances. Simulation results are conducted to show the effectiveness of the proposed control method.

Adaptive neural network anti-disturbance control of a cable-driven flexible-joint-based underwater vehicle manipulator system with dead-zone input nonlinearities via multiple neural observers

To achieve the requirements of lightweight, low energy consumption, and low inertia of an underwater vehicle manipulator system, a cable-driven manipulator is installed on the underwater vehicle to form a cable-driven flexible-joint-based underwater vehicle manipulator system (CDFJ–UVMS). The CDFJ–UVMS is a complex nonlinear system subject to model uncertainties, complex marine environment disturbances, and actuator dead-zone nonlinearity. To design track controllers, the CDFJ–UVMS dynamics is divided into two parts: known and unknown. Subsequently, a radial basis function neural network is adopted to approximate the unknown nonlinearity. A neural network performance observer is constructed, whose estimation error is then used to design a novel neural disturbance observer (NDO) to estimate the total disturbance. Finally, an adaptive neural network control method is proposed for the CDFJ–UVMS based on the NDO, neural network compensator, and neural performance observer. The stability of the closed-loop system is analyzed using the Lyapunov method. The proposed control algorithm is applied to a CDFJ–UVMS with two cable-driven joints and compared with other control methods to show the effectiveness of the proposed control algorithm.

Model-free control of underwater vehicle-manipulator system interacting with unknown environments

The underwater vehicle-manipulator (UVM) interaction tasks require that UVM can adapt to different environments and have good stability properties. However, the complicated structures of UVM and uncertainties of the environment lead to poor interaction. In addition, transient performance related to overshoot and convergence is required during the interaction to ensure the safety of the UVM and environment. In this paper, an inner-loop/outer-loop control formulation is developed for underwater vehicle-manipulator interacting with environment, subject to unknown system and environment dynamic. For the inner-loop, an approximation-free state feedback controller is designed for UVM system without a prior knowledge of dynamics, capable of guaranteeing trajectory tracking with prescribed performance. A prescribed function is constructed, which allows the convergence time and convergence accuracy to be predetermined. Moreover, it is independent of the initial conditions. For the outer-loop, an off-policy reinforcement learning algorithm is presented to optimize the parameters of the admittance model without requiring the information of environment dynamic. The proposed admittance optimization is feasible for arbitrary desired trajectory. Finally, simulation and experimental results validate the effectiveness of the proposed model-free tracking controller.

Autonomous Manipulation of an Underwater Vehicle-Manipulator System by a Composite Control Scheme With Disturbance Estimation

This article addresses an autonomous manipulation problem for an underwater vehicle-manipulator system (UVMS) operating in a free-floating way while subjecting to unknown continuous disturbance. More specifically, a composite control scheme composed of disturbance observer (DOB), predictor model network (PM-Net), and nonlinear model predictive control (NMPC), is devised to improve the control performance of UVMS (i.e., unicycle-like UVMS actuated only in the surge, heave, and yaw for vehicle body) in the case of disturbance, model mismatch, and input saturation. A RBF-DOB is formulated by combining a DOB and a Radial Basis Function (RBF) neural network to estimate disturbance at the current step. Then, the PM-Network, composed of a disturbance predictor network and state predictor network, is developed based on long short-term memory (LSTM) network that predicts UVMS state sequences considering model mismatch and disturbance. The NMPC is deployed as a feedback control law to endow the input saturation of the UVMS and produce optimal control action. Compared with conventional DOB control methods using feed-forward compensation of disturbance, the primary merit of the proposed approach is that the disturbance estimated by RBF-DOB is utilized in the PM-Net to predict future UVMS state sequences, which are exploited on the NMPC’s receding optimization. RGB0,0,0Finally, realistic simulation and relevant experiment are conducted to demonstrate the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation behind this article is the autonomous manipulation of an underwater vehicle-manipulator system subjected to unknown disturbance. However, it is not always feasible or straightforward to obtain the external disturbance and unmodeled dynamics for designing robust controllers. On the one hand, how to manipulate the disturbance into the designed controller to generate optimal control action rather than by using feed-forward compensation. On the other hand, the control input saturation often occurs in the UVMS control, especially under the disturbance rejection conditions, where it should be considered in the controller design. Currently, the predominant methods for UVMS control lack a control scheme that provides a complete and credible control strategy that takes the aforementioned issues into consideration. Motivated by the above analysis, this study provides a composite control scheme to deal with the dynamic uncertainties, unknown disturbance, and input saturation. RGB0,0,0The results of realistic simulation and relevant experiments demonstrate the effectiveness of the proposed method. Hopefully, our control method can provide valuable theoretical and technical guidance to practicing marine engineers for controller design.

Underwater Robots and Key Technologies for Operation Control

Over time, the utilization of the Underwater Vehicle-Manipulator System (UVMS) has steadily increased in exploring and harnessing marine resources. However, the underwater environment poses big challenges for controlling, navigating, and communicating with UVMS. These challenges have not only spurred the continuous advancement of related technologies, but also made the development of the UVMS even more captivating. This article firstly provides a review of development status of the UVMS and discusses the current limitations and future directions, and then reviews in detail the dynamic and hydrodynamic modeling methods, and analyzes the principles, advantages, and disadvantages of various approaches. Then, we try to review 2 key technologies of operation control methods, including underwater positioning and navigation technologies and vehicle-manipulator coordinated control approaches. Finally, a reasonable prospect for the future development of UVMS is given.

Integrated Target Recognition and Grasp Control for the Underwater Vehicle–Manipulator System

Integrated Target Recognition and Grasp Control for the Underwater Vehicle–Manipulator System

Adaptive sliding mode tracking control of underwater vehicle-manipulator systems considering dynamic disturbance

This paper proposes an adaptive sliding mode tracking control method with a nonlinear disturbance observer (NDO-ASMTC) to solve the trajectory tracking problem of the underwater vehicle-manipulator system (UVMS) in the presence of large dynamic uncertainties and severe external disturbances. The proposed NDO-ASMTC method employs an adaptive control law, an improved fractional-order sliding mode, and a nonlinear disturbance observer to achieve excellent tracking performance of the UVMS. To obtain a fast response speed in the sliding mode phase and eliminate static error in the reaching phase, a modified fractional-order sliding mode control scheme is proposed. An adaptive auto-tuning law of switching gains is presented to overcome the problem that the fixed switching gain may lead to obvious tracking performance degradation under lumped uncertainty. In addition, a nonlinear disturbance observer is implemented to maintain the robustness and stability of the control scheme under severe disturbances. Moreover, the proposed adaptive control scheme has been proved to be stable using the Lyapunov theorem. Simulations and experiments show that the proposed control scheme can meet the design requirements of the UVMS in terms of tracking accuracy, system stability, and interference attenuation under the application of external disturbances.

A robust optimal control by grey wolf optimizer for underwater vehicle-manipulator system.

Underwater vehicle-manipulator system (UVMS) is a commonly used underwater operating equipment. Its control scheme has been the focus of control researchers, as it operates in the presence of lumped disturbances, including modelling uncertainties and water disturbances. To address the nonlinear control problem of the UVMS, we propose a robust optimal control approach optimized using grey wolf optimizer (GWO). In this scheme, the nonlinear dynamic model of UVMS is deduced to a linear state-space model in the case of the lumped disturbances. Then, the GWO algorithm is used to optimize the Riccati equation parameters of the H∞ controller in order to achieve the H∞ performance criterion, such as stability and disturbance rejection. The optimization is performed by evaluating the performance of the closed-loop UVMS in real-time comparison with the popular artificial intelligent algorithms, such as as ant colony algorithm (ACO), genetic algorithm (GA), and particle swarm optimization (PSO), using feedback control from the physical hardware-in-the-loop UVMS platform. This scheme can result in improved H∞ control system performance, and it is able to ensure that UVMS has strong robustness to these lumped disturbances. Last, the validity of the proposed scheme can be established, and its performance in overcoming modeling uncertainties and external disturbances can be observed and analyzed by performing the hardware-in-the-loop experiments.

Improved finite-time prescribed performance super-twisting sliding mode united control framework for underwater vehicle-manipulator system

This paper investigates the trajectory tracking control problem for underwater vehicle-manipulator system (UVMS) with model uncertainties, dynamic coupling effects and external disturbances. Firstly, an improved finite-time performance function (IFTPF) is proposed to ensure the prescribed tracking accuracy with predefined convergence time while avoiding singularities in the system and enhancing robustness. Additionally, to reduce the controller’s dependence on the system model and compensate for unknown external disturbances, a neural network (NN) estimator is proposed to estimate the combination of system uncertainties and dynamic coupling effects, while a neural network-based nonlinear disturbance observer (NNDO) addresses the unknown external disturbances. Furthermore, an improved finite-time prescribed performance super-twisting sliding mode control framework using neural network-based disturbance observer (NNDO-IFTPPSTSMC) is proposed to ensure control accuracy and robustness while reducing chattering phenomenon. Theoretical analysis demonstrates that corresponding error variables of the system are bounded, and the trajectory tracking errors satisfy the prescribed performance constraints. Finally, simulation results validate the effectiveness and feasibility of the proposed method.

Impedance control based on error feedback for the manipulator of an underwater vehicle-manipulator system

Impedance control based on error feedback for the manipulator of an underwater vehicle-manipulator system

Enhancement and Optimization of Underwater Images and Videos Mapping

Underwater images tend to suffer from critical quality degradation, such as poor visibility, contrast reduction, and color deviation by virtue of the light absorption and scattering in water media. It is a challenging problem for these images to enhance visibility, improve contrast, and eliminate color cast. This paper proposes an effective and high-speed enhancement and restoration method based on the dark channel prior (DCP) for underwater images and video. Firstly, an improved background light (BL) estimation method is proposed to estimate BL accurately. Secondly, the R channel’s transmission map (TM) based on the DCP is estimated sketchily, and a TM optimizer integrating the scene depth map and the adaptive saturation map (ASM) is designed to refine the afore-mentioned coarse TM. Later, the TMs of G–B channels are computed by their ratio to the attenuation coefficient of the red channel. Finally, an improved color correction algorithm is adopted to improve visibility and brightness. Several typical image-quality assessment indexes are employed to testify that the proposed method can restore underwater low-quality images more effectively than other advanced methods. An underwater video real-time measurement is also conducted on the flipper-propelled underwater vehicle-manipulator system to verify the effectiveness of the proposed method in the real scene.

Robotic organism targets regional coverage capture path planning for marine aquafarm based on value iteration network

The application of robots in the field of Marine aquatic organisms capture can greatly improve the fishing efficiency and economy of seafood. The purpose for path planning of underwater vehicle manipulator system (UVMS) is to capture the most organisms in the shortest time with no obstacle collisions. This paper has proposed a novel cost modelling algorithm for the collision avoidance, manipulation and vehicle movement during the target capture process. On the underwater unknown and blurred environment, a novel double value iteration network with long short-term memory (LSTM) network has been proposed on the basis of value iteration networks. The proposed improved double value iteration network can predict the blurred and unknown environment, plan capture strategy with the calculated collision risk and movement cost. In the value iteration module, the use of LSTM network can improve the stability of training and the performance of the model. Moreover, a novel cooperative path planning model for task allocation has been proposed to improve capture efficiency. Simulations on robot and multiple robots capture planning have verified the network performance.

An Adaptive Control Method with Disturbance Estimation for Underwater Manipulation

In this paper, an adaptive control scheme with disturbance estimation is proposed for the station keeping and trajectory tracking of an underwater vehicle-manipulator system (UVMS) in a task space. The control of UVMSs encounters time-varying and lumped disturbances introduced by the motion of the manipulator and currents. The proposed control scheme includes an observer to identify disturbances without introducing additional sensors. Using state-of-the-art UVMS control technologies, it alleviates the restriction of the feedforward control applied to the UVMS. In addition, the proposed control scheme is not designed based on the worst case. In other words, the robustness of the controller is not achieved at the price of performance, and the performance of the controller can be recovered in the absence of disturbance. Meanwhile, the controller can also realize quick responses to attenuating disturbance and closed-loop stability when encountering an unexpected disturbance. The effectiveness of the proposed control scheme was demonstrated by a series of spatial trajectory tracking experiments under various disturbances. A limitation of this study is that the proposed control scheme is specialized for the underwater manipulation of UVMSs.

Adaptive fuzzy control of underwater vehicle manipulator system with dead-zone band input nonlinearities via fuzzy performance and disturbance observers

In this paper, an adaptive fuzzy control design problem is investigated for an underwater vehicle manipulator system (UVMS) based on a fuzzy performance observer (FPOB) and fuzzy disturbance observer (FDO). The UVMS is considered as a nonlinear system comprising model uncertainties, external disturbances and dead-zone band input nonlinearities. To mitigate the combined effects of the dead-zone and the hysteresis, a novel pre-deadzone compensator is proposed. Then, fuzzy logic systems (FLSs) with online adaptations are utilized to evaluate the unknown components of the inertia matrix, the Coriolis matrix and the damping matrix. In addition, a fuzzy performance observer is constructed whose errors are used to estimate the external disturbances. Further, an H∞ fuzzy control technique is developed to reduce the errors in estimating the external disturbance. Then, the stability and tracking performance of the closed-loop system are analyzed using the Lyapunov stability theory. It is shown that all signals of the closed-loop system are uniformly ultimately bounded. Finally, simulations are performed to demonstrate the effectiveness of the proposed control scheme in addressing the tracking control problem of the UVMS in presence of the dead-zone band and disturbances.

Development and Control of an Innovative Underwater Vehicle Manipulator System

Recently, as humans have become increasingly interested in ocean resources, underwater vehicle-manipulator systems (UVMSs) have played an increasingly important role in ocean exploitation. To realize precise operation in underwater narrow spaces, the fly arm underwater vehicle manipulator system (FAUVMS) is proposed with manipulators as its core. However, this system suffers severe dynamic coupling effects due to the combination of small vehicle and big manipulators. To resolve this issue, we propose a robust adaptive controller that contains two parts. In the first part, the extended Kalman filter (EKF) is designed to estimate the system states and predicts external disturbances to achieve adaptive control. In the second part, a chattering-free sliding mode control (SMC) is designed to converge the tracking errors to zero, thus guaranteeing the robustness of the controller. We constructed the simulation platform based on the geometric model of FAUVMS, and various simulations are carried out under different situations. Compared to the traditional methods, the proposed method has a faster convergent speed, a better robustness and adaptiveness to external disturbances, and the tracking errors of positions of the vehicle and each end-effector are much smaller.

Fully Automatic Visual Servoing Control for Underwater Vehicle Manipulator Systems Based on a Heuristic Inverse Kinematics

The use of underwater vehicle manipulator systems (UVMS) equipped with cameras has gained significant attention due to their capacity to perform underwater tasks autonomously. However, controlling both the manipulator and the remotely operated vehicle (ROV) based on the vision system information is not an easy task, especially in situations where the vehicle cannot be parked/held stationary. Most of the existing approaches work based on complex matrix calculations for the inverse kinematics (IK), which can lead to high computational costs and the need to deal with singularity problems. A problem arises when the amount of time needed to calculate the UVMS configuration can result in reduced frequency of target pose estimation, beyond the point where the target has moved out of the camera field of view. Therefore, this paper proposes an autonomous visual servoing approach for UVMS, including an extension of a heuristic technique named M-FABRIK (Mobile - Forward and Backward Reaching IK) to calculate the UVMS inverse kinematics in a simple and fast way. This approach aims to control both the configuration of the manipulator and ROV position in order to allow underwater intervention in situations where the ROV cannot be parked/held stationary. This solution allows the vehicle to be positioned according to additional criteria, besides avoiding matrix inversion and being robust to singularities. Trials have been performed with a manipulator mounted on a work-class ROV for an autonomous underwater monitoring task and results demonstrate a simple and fast approach, which is able to set the configuration of the manipulator as well as the ROV for visual servoing applications in real-time, such as for monitoring, tracking and intervention tasks underwater.

Reinforcement Learning for Autonomous Underwater Vehicles via Data-Informed Domain Randomization

Autonomous Underwater Vehicles (AUVs) or underwater vehicle-manipulator systems often have large model uncertainties from degenerated or damaged thrusters, varying payloads, disturbances from currents, etc. Other constraints, such as input dead zones and saturations, make the feedback controllers difficult to tune online. Model-free Reinforcement Learning (RL) has been applied to control AUVs, but most results were validated through numerical simulations. The trained controllers often perform unsatisfactorily on real AUVs; this is because the distributions of the AUV dynamics in numerical simulations and those of real AUVs are mismatched. This paper presents a model-free RL via Data-informed Domain Randomization (DDR) for controlling AUVs, where the mismatches between the trajectory data from numerical simulations and the real AUV were minimized by adjusting the parameters in the simulated AUVs. The DDR strategy extends the existing adaptive domain randomization technique by aggregating an input network to learn mappings between control signals across domains, enabling the controller to adapt to sudden changes in dynamics. The proposed RL via DDR was tested on the problems of AUV pose regulation through extensive numerical simulations and experiments in a lab tank with an underwater positioning system. These results have demonstrated the effectiveness of RL-DDR for transferring trained controllers to AUVs with different dynamics.

Adaptive Fractional-Order Fast-Terminal-Type Sliding Mode Control for Underwater Vehicle-Manipulator Systems

Abstract This article proposes an adaptive fractional-order fast-terminal-type sliding mode control method to solve the trajectory tracking problem of the underwater vehicle-manipulator system in the presence of large dynamic uncertainties and strong external disturbances. For improving the control continuity and accuracy of the conventional sliding mode control method, a modified fractional-order sliding mode control scheme with a novel fractional-order sliding manifold and a fast-terminal-type reaching law is proposed. In addition, an auto-tuning law of switching gains is presented and combined with the fractional-order sliding mode control scheme to overcome the problem that the fixed switching gain may lead to obvious tracking performance degradation under complicated lumped disturbances. Finally, the superiorities of the proposed adaptive fractional-order fast-terminal-type sliding mode control method are verified through a series of comparative simulations of a 6-degrees-of-freedom (DOF) UVMS carrying a 6DOF manipulator.

Task-Priority Operational Space Control for Vehicle-Manipulator Systems with Modelling Errors

The dynamics of underwater vehicle-manipulator systems (UVMSs) are very hard to model, which reduces the feasibility of model-based control approaches. Even so, such strategies prove useful in redundancy resolution. In this paper, higher-order sliding mode control is combined with task-priority operational space control (OSC) in order to handle and utilize the redundancy of UVMSs despite the presence of dynamic model errors. At each task level, the generalized super-twisting algorithm is implemented to reject effects caused by model errors while maintaining a continuous control signal. The general problem of OSC with uncertain models is analyzed, and some of its challenges are highlighted, including an algebraic loop. We conduct a simulation study on a highly redundant UVMS, where we compare task-level higher-order sliding mode control to proportional-derivative control. Though this paper is motivated by challenges specific to UVMSs, the results also hold for other vehicle-manipulator systems.