This paper presents a novel trajectory tracking control method for bioinspired undulating robotic systems with quadratic performance and input constraints. First, the relationship between the fin oscillation frequency and the generated thrust is established through dynamic analysis. Based on this, a constrained optimal tracking control framework is formulated by integrating duality theory with discrete-time dynamic programming. To solve the dual-constrained optimization problem, a learning algorithm is developed, which employs an exterior penalty function method combined with a simplex search strategy. The convergence precision is used to determine the optimal parameters, leading to an analytical expression for the control law. Finally, experimental results demonstrate the effectiveness and feasibility of the proposed control method.

Underwater humanoid robots hold great potential for complex marine tasks thanks to their dexterous and versatile hands. However, their perception capabilities are severely hindered in turbid and low-light environments, where vision-only sensing becomes unreliable. To address this challenge, we present HydroPalm, the first bionic dual-modal visual-tactile sensor designed for hands of underwater humanoid robots. HydroPalm integrates a wide-field binocular vision module with a high-resolution soft tactile interface. Specifically, an iterative concentric angular topology sorting (ICATS) algorithm is proposed to resolve marker-matching ambiguity caused by background distortions. A contact-based refractive stereo ray tracing (CRSRT) method is introduced to perform accurate 3D reconstruction in water with variable refractive indices. Experiments across 0-1285.2 NTU demonstrate that HydroPalm improves reconstruction quality by over 230% compared to vision-only baselines, while maintaining a mean absolute error below 5% in waters with varying refractive indices. When deployed on a robotic hand, HydroPalm further enables reliable grasping inside a fully dark and highly turbid underwater cavity. The results suggest a new dual-modal sensing paradigm tailored for underwater humanoid robots, with promising applications in seafood harvesting, delicate ecological sampling, and archaeological excavation.

Robust and efficient tracking and mapping are critical for underwater vehicles, but remain challenging due to degraded visual quality, ambiguous features, and limited computational resources. Although recent deep learning-based stereo matching methods have significantly improved geometric perception for robots, most existing approaches struggle to simultaneously achieve high speed and strong generalization. To address these challenges, we propose SAFT, a tracking and mapping framework based on self-supervised, robust, and real-time stereo matching. SAFT introduces three key innovations: 1) SAFT-Stereo, a novel stereo matching network that integrates cost aggregation with iterative optimization to enable efficient disparity estimation in feature-sparse regions; 2) a spatiotemporal self-supervised loss that leverages both spatial and temporal constraints to provide stable training signals in textureless regions; and 3) SAFT-DSOL, a real-time tracking and mapping algorithm that integrates the self-supervised models to achieve robust localization and dense reconstruction. Extensive experiments on both public and custom underwater datasets demonstrate that SAFT-Stereo achieves the best generalization performance among all real-time methods, while requiring only 1/6 of the inference time of RT-IGEV++. Moreover, the proposed SAFT-DSOL enables stable and efficient tracking and achieves real-time dense reconstruction in indoor shipwreck scenarios. The code is available at github.com/c237814486/SAFT-Stereo.

Tactile sensing is indispensable for underwater operations, as it provides critical feedback on contact information and environmental interactions. Although existing sensors, such as optical, piezoelectric devices, have been employed for underwater tactile perception, they exhibit some limitations in multi-physical field contact perception. To address these challenges, this study introduces the ViTac-Gripper, a vision-based tactile sensing underwater gripper designed to enhance underwater perception and grasping capabilities. The Finite Element Method (FEM) is utilized to simulate the deformation and force distribution on the tactile surface, providing a high-fidelity model for contact behavior analysis. A domain-aligned multi-physical information perception network framework is proposed, which effectively bridges the simulation-to-reality gap and enables robust extraction of tactile information, including normal/shear force, contact position, 3D Reconstruction and dense force distribution. Experimental results demonstrate the system’s ability to accurately reconstruct multi-physical contact information, while the adaptive grasping strategy ensures stable and reliable object manipulation. The ViTac-Gripper represents a significant advancement in underwater robotics, offering a cost-effective and versatile solution for underwater manipulation tasks.

Underwater vehicle-manipulator systems (UVMSs) play crucial roles in the fields of underwater target monitoring and pipeline maintenance. However, achieving accurate tracking for underwater pipelines is challenging due to the complexity of UVMSs in terms of nonlinearity, strong coupling and underactuation. To solve the aforementioned problems, an underwater biomimetic vehicle-manipulator system (UBVMS) and an underwater pipeline tracking control method based on the robot vision are proposed. The UBVMS is equipped with the biomimetic undulatory fin propulsors and the biomimetic flipper propulsors, which are inspired by the median and/or paired fin propulsion mode and the body and/or caudal fin propulsion mode of fishes, respectively. The biomimetic undulatory fin propulsors provide the UBVMS with advantages of maneuverability and stability, while the biomimetic flipper propulsors enable the UBVMS to have improved acceleration ability. A tracking control algorithm with adaptive weight coefficients is designed to improve the pose stability of the UBVMS. A fuzzy rule mapping model is constructed to describe the nonlinear relationship between the biomimetic propulsors' control parameters and the propulsive force/torque. Finally, four types of pipeline tracking experiments are conducted to verify the effectiveness and feasibility of the proposed UBVMS and control algorithm.

The complex underwater environment presents numerous challenges for the design of soft grippers, which often suffer from limited load capacity, poor stability, low portability, and imprecise control. This paper proposes a novel rigid-soft hybrid gripper specifically designed for underwater use. The gripper's finger is constructed from silicone, reinforced with a multi-link rigid exoskeleton on the outside, and actuated by tendons. This design provides three key advantages: compliance (capable of handling fragile objects such as a piece of tofu), heavy lifting (demonstrated by lifting an 80 kg barbell with three fingers), and precise, stable operation (the hybrid gripper maintains its shape despite water flow disturbances). Additionally, the gripper is compact and lightweight, with the driving system powered by just four 23g servo motors, making it easy to mount on various underwater robots. To enable precise control, both specialized kinematic and mechanics models were developed, allowing accurate predictions of the relationships among tendon displacement, exoskeleton deformation, soft material deformation, and tendon tension. This study thoroughly considers the challenges of underwater environments, offering new insights for advancing the field of underwater soft grasping.

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. Finally, realistic simulation and relevant experiment are conducted to demonstrate the effectiveness of the proposed method. Note to Practitioners—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. The 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.

This paper addresses a learning-based path following control scheme for a biomimetic underwater vehicle (BUV) driven by undulatory fins. A dynamic line-of-sight (DLOS) guidance system is designed, which uses a virtual ball with a dynamic radius to detect the reference path. This DLOS system guides our BUV in the path following control and extracts essential information for the Markov decision process (MDP) of the control task. A deep reinforcement learning (DRL) algorithm, sample-observed soft actor-critic (SOSAC) is proposed. The can train out control policy with greater cumulative reward and higher success rate by using two tricks: sample observation and sample diversification. Based on the DLOS system, the MDP of the control task, and a multilayer perceptron (MLP) trained by the SOSAC, our control scheme is established. Experiments show that our BUV can successfully achieve path following control in an indoor pool environment by using this control scheme.Note to Practitioners—The motivation of this paper is to design a practical end-to-end path following control scheme for the BUV driven by undulatory fins, and verify this scheme in a real-world environment. Unlike common autonomous underwater vehicles (AUVs) using axial propellers, the BUVs apply biomimetic propellers such as the undulatory fin. Multimodel wave patterns can be implemented by the undulatory fin, which generates nonlinear thrust and lateral force simultaneously. This propulsive feature makes the driving force on different directions of the BUV to be strong coupled, and it is complicated to convert the outputs of a common controller into waveform parameters of the undulatory fins to control the BUV. Therefore, in this paper, we proposed an end-to-end learning-based path following controller, which observes environmental information and directly generates waveform parameters to control our BUV. Experiments suggest that our control scheme is practical and valid.

With the advantages of natural flexibility, large force-weight ratios, and green cleanliness, pneumatic artificial muscle (PAM) actuators that mimic biological skeletal muscles have attracted much attention. However, the inherent defects of PAMs, such as high nonlinearities, limited contraction lengths and frequencies, and multiple input constraints, pose significant challenges to the motion control of PAM-actuated parallel robots; meanwhile, most existing methods do not take into account motion constraints and working efficiency. To this end, a prescribed-time adaptive fuzzy motion control method is developed in this article, where PAM-actuated parallel robots can accurately achieve prescribed tracking performance within an allowable input pressure range. In particular, regardless of the initial values of target trajectories, the expected tracking accuracy is achieved within the prescribed time by restricting the tracking errors to the improved performance constraints; also, the motion velocities remain within the preset dynamic constraints, thereby improving the working safety and efficiency. To the best of authors' knowledge, this article presents thefirstadaptive fuzzy motion control method for PAM-actuatedparallelrobots, which cansimultaneouslyachieve motion constraints and prescribed tracking performance. Moreover, the stability of all signals is proved through theoretical analysis, and then the effectiveness of the proposed method is fully verified by a series of hardware experiments.

This article presents the design and implementation of an innovative biomimetic underwater vehicle (BUV) and its locomotion controller. Through mimicking a dactylopteridae, the hybrid propulsion BUV is designed with two symmetrical bio-inspired long-fins and a double-joint fishtail. The mechatronic design of the dactylopteridae-inspired BUV with the pectoral long-fins and a double-joint fishtail is first provided. The two flexible long-fins compose the median and/or paired fin (MPF) propulsion, while the fishtail acts as the body and/or caudal fin (BCF) propulsion. Through the coordination of BCF and MPF propulsion modes, the BUV obtains excellent low-speed locomotion stability and also keeps high maneuverability. Moreover, the locomotion control methods based on central pattern generators (CPGs) model and fuzzy adaptive proportion integral differential (PID) are proposed for this BUV. In the end, the experimental results of the multimode motion and closed-loop motion control demonstrate the feasibility and effectiveness of the mechanism and the locomotion control system.Note to Practitioners—The motivation behind this article is the design of a novel biomimetic underwater vehicle (BUV) that possesses low-speed locomotion stability and fast swimming ability, which is suitable for carrying relevant sensors to complete water quality monitoring, biological observation, underwater equipment inspection, underwater structure detection, and other marine tasks. Currently, BUVs are usually designed as only one propulsion mode by caudal fin or paired fins, which makes it difficult to have the advantages of both modes. In order to further study the problem, we designed a dactylopteridae-inspired BUV with the bilateral pectoral long-fins (providing low-speed locomotion stability) and a double-joint fishtail (providing fast swimming ability). A hybrid-driven motion control framework is presented for the BUV based on a central pattern generators (CPGs) model and fuzzy adaptive proportion integral differential (PID). A series of experiments suggests that the mechanism and the locomotion control system are practical and valid. Hopefully, our mechanism and control framework can provide valuable theoretical and technical support guidance to the practicing marine engineer for the codesign of propulsion mode and control.

In this article, the underwater target tracking control problem of a biomimetic underwater vehicle (BUV) is addressed. Since it is difficult to build an effective mathematic model of a BUV due to the uncertainty of hydrodynamics, target tracking control is converted into the Markov decision process and is further achieved via deep reinforcement learning. The system state and reward function of underwater target tracking control are described. Based on the actor-critic reinforcement learning framework, the deep deterministic policy gradient actor-critic algorithm with supervision controller is proposed. The training tricks, including prioritized experience replay, actor network indirect supervision training, target network updating with different periods, and expansion of exploration space by applying random noise, are presented. Indirect supervision training is designed to address the issues of low stability and slow convergence of reinforcement learning in the continuous state and action space. Comparative simulations are performed to show the effectiveness of the training tricks. Finally, the proposed actor-critic reinforcement learning algorithm with supervision controller is applied to the physical BUV. Swimming pool experiments of underwater object tracking of the BUV are conducted in multiple scenarios to verify the effectiveness and robustness of the proposed method.

Biomimetic underwater robots have attracted considerable research attention globally, owing to their quieter actuations, higher propulsion efficiency, and stronger maneuverability when compared with conventional underwater vehicles equipped with axial propellers. This article provides a comprehensive survey of current research in this field. First, we review the development status of biomimetic underwater robots in both body/caudal fin (BCF), median/paired fin (MPF), and their hybrid propulsion modes. Then, we outline the motion control methods employed in biomimetic underwater robots, including open-loop swimming control and typical closed-loop control strategies. In particular, we detail our latest studies on the RobCutt series underwater robots. On this basis, some critical issues and future directions are summarized. We predict that biomimetic underwater robots will have excellent prospects in underwater environment exploration and resource utilization.

This article addresses a problem of seabed terrain following control (STFC) for an underwater vehicle-manipulator system (UVMS). The motivation is to perform a visual search of marine products closely to seabed in unknown environment. In terms of this issue, we propose a novel and robust STFC framework for our UVMS to maintain an appropriate height to seabed. A nonlinear model predictive control (NMPC) method is formulated to solve the STFC problem. To relieve online computational burden and system noisy influence, Ohtsuka's continuation/generalized minimal residual (C/GMRES) algorithm incorporated with a tracking differentiator (TD) is investigated. In order to improve the following accuracy, the system state prediction part of the NMPC and a long short-term memory (LSTM) network are elaborated to predict future seabed terrain using a depth gauge and an altimeter, respectively. Finally, the three different physical scenarios for STFC problem are established using ROS to demonstrate the robustness and efficiency of the proposed algorithm.

This article presents a coordinated vehicle-manipulator control method for an underwater biomimetic vehicle-manipulator system (UBVMS) to implement floating autonomous manipulation in practice. An algorithm framework composed of adaptive tracking differentiator (ATD), extended state observer (ESO), improved nonsingular terminal sliding-mode control (I-NTSMC), fuzzy-logic controller (FLC), and estimator of manipulator disturbances, is proposed. The ATD is designed to generate desired motion state and alleviate noise. The ESO is developed to estimate the motion state, systematic uncertainties, and external disturbances. The proposed I-NTSMC method assures the finite-time convergence of the system states and alleviate chattering. The estimation of the manipulator disturbances is incorporated into the control strategy to enhance the station keeping of the vehicle. Finally, underwater autonomous free floating manipulation experiments about opening a door and grasping objects are conducted to validate the theoretical results and confirm the feasibility of the proposed control strategy.

This paper aims to investigate optimal formation solutions of multiple biomimetic underwater vehicles (BUVs). The BUV is propelled by undulatory fins on both sides, and can perform various locomotion patterns, especially turning in situ and diving vertically. Firstly, the optimal formation problem is formulated, followed by theoretical analysis of a special case of optimal line formation. Then, a solution is proposed from the perspective of evolutionary computation. In particularly, the coordinates and the slope of the desired line formation, together with the pairings between initial positions and target positions, are obtained based on particle swarm optimization. Furthermore, we demonstrate the validity of this method by comparing the simulation results with the results of theoretical analysis. Finally, simulations results of multiple BUVs verify the feasibility of the proposed optimal formation methods.

This article presents the comprehensive framework for a hybrid-driven underwater vehicle-manipulator system (HD-UVMS) to grasp marine products on the seabed. The purpose of the proposed hybrid-driven propulsion system is to improve the swimming ability of the HD-UVMS by using thrusters and enhance the stability of its pose adjustment mechanism via two unique long fin propulsors. The control mode for the thrusters and long fin propulsors is based on a fuzzy logic control method. Subsequently, a lightweight manipulator is developed to grasp marine products. The open-closed angle and current controls for the gripper help to avoid damaging marine products. A vision system is installed to enable the HD-UVMS to gradually approach marine products with the aid of monocular vision and grasp them with the aid of binocular vision. A detailed method for monocular passive ranging and stereo matching, in accordance with real-time metrics, is elaborated. Finally, relevant experiments are conducted in an indoor pool and under real sea condition to assess the effectiveness of the proposed framework. Note to Practitioners-The motivation behind this article is the design of an underwater vehicle-manipulator system that can grasp marine products on the real seabed and perform other underwater intervention tasks. Currently, the predominant method of fishing for marine products relies on human divers, which has disadvantages for human divers' health due to the long periods of time spent working underwater. In order to further study the problem, this article develops a hybrid-driven underwater vehicle-manipulator system (HD-UVMS) to work in a real seabed environment. A hybrid-driven motion control framework is presented using the thrusters to achieve effective cruising and searching for marine products and long fin propulsors for the fine pose adjustment required to grasp marine products. The proposed lightweight underwater manipulator can grasp marine products on the seabed with the aid of a vision system. A series of experiments suggests that the HD-UVMS is practical and valid.

Boundary control strategy is developed to analyze the vibration problem of the offshore ocean thermal energy conversion (OTEC) system as well as to constrain the bottom tension and top motion. To provide an accurate dynamic behavior for the OTEC system, this distributed parameter system is modeled and formulated with a governing equation and boundary conditions (PDE-ODEs model). Two robust adaptive boundary controllers are designed and disposed at the endpoints of the system, and the stability of the controlled system under unknown disturbances is achieved. After selecting the relevant parameters appropriately, the offset of the offshore OTEC system can be suppressed to equilibrium position. Finally, the effectiveness of the proposed control is illustrated by simulation.

This paper addresses the problem of path following for biomimetic underwater vehicles (BUVs) propelled by undulatory ribbon-fins. First, the general kinematics and dynamics models of underwater vehicles are presented, followed by a fuzzy logic model for dealing with a nonlinear relationship between the propulsive force/torque and the control parameters of the undulatory fins of the BUV. Then the path following problem of the BUV is formulated. A path following control paradigm integrating the line-of-sight guidance system with backstepping (BP) technique is proposed to maneuver the BUV to follow a predefined parameterized curve without time constraints. The stability of the BP controller is analyzed and guaranteed by Lyapunov stability theory. Finally, simulations and experimental results illustrate the performance of the proposed path following control paradigm.

This paper addresses the problem of path following for a biomimetic underwater vehicle (BUV) propelled by undulatory fins with uncertain model and unknown disturbance. The mechanical structure of the BUV is briefly described. Moreover, the general kinematics and dynamics models of the vehicle are presented and the path following problem is formulated. The controller combining line-of-sight (LOS) guidance system with active disturbance rejection control (ADRC) technique is designed to maneuver the BUV to follow a predefined parameterized curve. Specifically, a guidance system based on LOS principle is implemented to decouple the multi-variable system to steer the surge speed and the course respectively. Furthermore, in order to deal with model uncertainty, ADRC is used in development of the surge speed controller and the course controller. Finally, simulations and experimental results validated the performance of the proposed path following control scheme.