For field tasks, it is quite challenged to operate in a complex environment for the underwater robots, especially for those with multiple control surfaces due to different response and gain characteristics. To this end, this paper develops a highly integrated robotic dolphin followed by a robust motion control system. For better maneuverability and fault-tolerant capabilities, a newly-designed robotic dolphin is presented, owning a wide array of sensors and multiple control surfaces, in which passive flukes are particularly applied. On this basis, a robust motion control system is proposed, including a depth controller based on velocity-related allocation strategies and a heading controller based on clearance compensation. In detail, considering the degradation of motion performance caused by passive flukes, a sliding mode controller for gain uncertainty and an allocation-related parameter tuning strategy for inputs response characteristics are designed. Extensive simulations and aquatic experiments are conducted, and the obtained results demonstrate the satisfied maneuverability of the designed prototype and the effectiveness of the proposed methods. This study can lay a foundation for further development of robotic dolphins with a robust motion system to execute complex tasks in the field.Note to Practitioners—This paper is inspired by the issue of robust motion control system for a newly-designed practical robotic dolphin that possesses a passive tail and redundant control surfaces. The traditional methods are usually susceptible to uncertainties in the passive tail gain, exhibiting degraded control performance. Moreover, control oscillations and slow convergence speed often occur caused by neglecting the characteristics of different control surfaces, including response patterns and clearance. This paper suggests a robust depth controller based on velocity-related allocation strategies and a robust heading controller based on clearance compensation. Specifically, an allocation-related parameter tuning strategy is given by considering inputs response characteristics, including response speed, saturations, and hydrodynamic force variation patterns. To guarantee fine regulations of heading control, a nonlinear disturbance observer (NDOB)-based clearance compensation is proposed. Extensive aquatic experiments on the newly-designed robotic dolphin verified the effectiveness of the proposed methods. It is envisioned that all these presented results can provide valuable engineering practice insights for industry practitioners.

Over prolonged evolution, natural fish possess exceptional maneuverability. For underwater robotic fish, maneuverability is a crucial performance metric during locomotion. This paper proposes a turning control optimization framework for a multi-flexible-joint bionic robotic fish. Firstly, three distinct turning strategies are devised based on kinematic analysis of the flexible robotic fish’s turning motion. Experimental results indicate that the control strategies need to be adjusted with respect to motion frequency to achieve optimal performance for the flexible robotic fish. Furthermore, an optimization problem incorporating dynamic constraints and cost functions of the flexible robotic fish is constructed. Based on a Constrained Iterative Linear Quadratic Regulator (CILQR), a turning performance optimization method is designed. Subsequently, optimal control strategies under both stationary and motion states are derived, effectively enhancing the turning performance of the flexible robotic fish. Finally, simulation and experimental results validate the effectiveness of the designed method. The developed flexible robotic fish could achieve a maximum swimming speed of 1.63 BL/s (body lengths per second) and a maximum average turning speed of 130.5°/s, offering guidance for the practical application of flexible robotic fish in aquatic environments.

Directly learning the swimming behaviors of real fish can significantly enhance the swimming performance of bionic robotic fish. This paper presents a novel transfer learning method based on a dynamic trajectory control approach for the robotic fish to learn swimming skills from real fish. First, we develop a fish motion capture system and a crucial motion extraction approach to realize precise decomposition of fish motions and collect abundant meaningful features from a snakehead fish as pre-training data. Next, we construct a two-stage transfer learning method based on Deep Deterministic Policy Gradient (DDPG), including an offline and an online stage. Specifically, in the offline stage, the obtained pre-training data is processed for experience learning within a DDPG-based network, whereas in the online stage, a dynamic trajectory tracking method is utilized to refine the robotic fish’s motions in real time based on the learned strategies. Experimental results on a self-developed four-joint robotic fish show that the proposed method effectively extracts and transfers biological motion features into the motion control of the robotic fish. Compared to the conventional CPG method, the proposed approach exhibits stronger acceleration capabilities and more efficient swimming, resulting in enhanced maneuverability of the robotic fish. Overall, this approach provides a technical foundation for bionic robotics to learn from nature.

In nature, fish have evolved sophisticated muscular systems that enable them to dynamically regulate their body movements for efficient and agile swimming, which has inspired the development of compact and fast flexibility regulation mechanisms in robotic fish. While existing robotic fish have primarily relied on passive flexible mechanisms and tunable stiffness mechanisms, these approaches often lack the dynamic adjustment capabilities that are characteristic of living fish. This article proposes a novel biomimetic flexible fishtail capable of dynamically controlling its deformation through artificial muscles made from macrofiber composite. In detail, the fishtail is equipped with a servo motor as the sole driving joint, while the artificial muscles regulate the deformation to indirectly adjust stiffness. A dynamic model considering both flexibility and hydrodynamics is established, and a partial differential equation observer is particularly developed to estimate the tail's full states. Subsequently, a deformation control framework incorporating a deep reinforcement learning strategy is constructed and successfully deployed on an embedded platform via lightweight design. Simulation and experimental results validate the accuracy and effectiveness of the dynamic model, observer, and control strategy. Especially, the proposed fishtail demonstrates the ability to enhance propulsion in fishlike swimming modes across various frequencies, ranging from 15% to 203%. When assembled into an untethered robotic prototype, deformation control allows the prototype's swimming speed to vary, achieving up to 42% slower or 37% faster speeds compared to passive compliance. Its rapid adjustability and adaptability to different frequencies represent significant advancements not widely reported in previous studies. The obtained results will offer some significant insights for flexible robotic systems to enhance their agility and interactivity.

Due to the complex mechanism and fabrication process of flexible materials, it remains extremely challenging for a flexible robotic fish to achieve fast and efficient locomotion. In this article, taking advantage of the passive bending and energy storage properties of flexible materials, we propose an untethered robotic fish with multiple flexible joints to achieve high performance and low Cost of Transport (COT). First, combining rigid links and flexible materials, a compact flexible tail with a simple and efficient structure is proposed. Next, the pseudo-rigid body theory is applied to analyze the deformation of passive joints, and a full-state dynamic model is established. More importantly, an optimization method by adjusting the phase differences of the passive joints is used to obtain high aquatic performance. Finally, extensive simulations and experiments validate the effectiveness of the proposed method, and the robotic fish can achieve a maximum speed of 1.63 body length (BL) per second and a minimum COT of 4.8 J/m (2.87 J/m$\cdot$kg). Compared with the multi-joint robotic fish with a similar design, the COT is reduced by up to 81.05% with the basically same aquatic ability. Excitingly, the flexible robotic fish can achieve a COT of 7.36 J/m at 1.23 BL/s, which is 15.72%-36.34% lower than that of the bluefin tuna and is within the range of yellowfin tuna, offering valuable insight into high speed and long endurance applications for underwater robots.Note to Practitioners–This paper is motivated by the design and optimization of an efficient bionic flexible underwater robot with high aquatic abilities, which is conducive to aquatic tasks that require long-time and long-distance sailing, such as underwater topographic exploration, submarine archaeology, and underwater search and rescue. Existing studies of free-swimming bionic underwater robots usually focus on the improvement of swimming speed and rarely consider achieving both high swimming performance and low energy cost. Thus, this paper proposes a bionic underwater robot design with two joints made of flexible materials on the tail to address the problem. Based on detailed analyses of the hydrodynamic force and flexible joint deformation, we propose an effective optimization method for swimming performance. A series of simulations and experiments suggest that the mechatronic design and optimization method are practical and valid. Hopefully, our design and method can provide theoretical guidance for engineers to design and optimize robots with flexible joints.

Artificial lateral line (ALL) sensors hold the potential to enhance the perception abilities of robotic fish by capturing surface pressure gradients and identifying near-field object, such as dipole source. However, the robotic fish’s free-swimming motion introduces periodic low-frequency noise into the ALL data, while dipole sources with time-varying positions generate pressure signals with complex time-frequency characteristics. This paper proposes a complete solution to these challenges that would enable freely swimming robotic fish to locate dipole source. Firstly, an ALL system consisting of pressure sensors is integrated into the robotic fish, further constructing a real-time data acquisition and processing system. Secondly, to effectively estimate and remove the swimming-induced noise from the ALL data, a noise estimation model is developed based on the bionic motion mode and unsteady Bernoulli equation. Subsequently, short-time Fourier transform is applied to the high-quality data after noise elimination, followed by developing a convolution regression neural network for feature extraction and dipole source localization. Finally, extensive simulations and experiments are conducted to validate the effectiveness of the proposed methods and perform the positive impact of the noise estimation model. Remarkably, within the range of perception, the average accuracy of dipole source location can reach 13.6 mm, providing a promising reference for improving the perception abilities of underwater robots.Note to Practitioners—This paper is motivated by the problem of blind zones in near-field perception of underwater robots. The existing perception methods as visual sensing are limited by the dark and cloudy underwater environment, and are powerless in near-field localization. In addition, the artificial lateral line, as a potential near-field sensor, is challenging to be applied in self-propelled robots due to the swimming noise. This paper proposes an integrated near-field sensory system that includes an ALL sensor, a swimming noise elimination method and a dipole source localization method. Specifically, a fish-inspired ALL sensor is designed by high-accuracy pressure sensors and integrated into the robotic fish. To enhance localization performance, a swimming noise elimination model is constructed based on unsteady Bernoulli equation. Furthermore, a convolution regression network is developed for accurate localization of near-field objects. A series of simulations and experiments demonstrate the effectiveness and superiority of the proposed near-field sensory system. Hopefully, our proposed methods can provide valuable guidance and support for near-field object localization to improve the intelligent operation ability of underwater bionic robots, such as cooperative control, underwater navigation, environment exploration, and so forth.

The application potential of robotic fish embedded with intelligent visual navigation algorithms in underwater autonomous operation is on full display recently. However, the existing visual navigation methods are limited by the underwater visual conditions and the motion characteristics of robotic fish. To this end, this paper proposes a novel autonomous navigation framework integrated with visual stabilization control. In practice, a stereo vision-based navigation network is proposed to generate the guidance law. On this basis, a biomimetic robotic hammerhead shark with a controllable cephalofoil is developed, and a nonlinear model predictive controller for cephalofoil stabilization relying on the dynamic model is elaborately designed. Extensive simulations and underwater experiments are conducted to validate the effectiveness and superiority of the proposed methods, which significantly enhance exploration efficiency and reduce image jitter by 26.02% compared to the traditional methods. The obtained results provide a new idea for underwater robots to autonomously explore the ocean.Note to Practitioners—This paper is motivated by the problem of vision-based underwater autonomous navigation for a biomimetic robotic fish that possesses underwater visual stability and good maneuverability. The existing visual navigation networks usually generate unexpected navigation instructions when dealing with complex or ambiguous underwater scenes. Additionally, image jitter caused by the rhythmic motion of robotic fish can lead to navigation failure. This paper suggests an integrated navigation framework that includes a biomimetic platform design, a visual stabilization controller, and an intelligent underwater navigation network. Specifically, a novel sphyrnidae-inspired robotic shark is designed as a new platform with superior motion performance. To enhance underwater visual stability, a nonlinear model predictive control-based visual stabilization controller is proposed. Furthermore, a deep stereo attention navigation network based on a parallax attention mechanism is proposed to improve the generalization of vision-based autonomous navigation. A series of underwater search experiments on the robotic shark demonstrate the effectiveness and superiority of the proposed navigation framework. Hopefully, our proposed methods can provide valuable guidance and support for universal underwater robot navigation to accomplish practical marine tasks, such as underwater rescue, resource exploitation, biological observation, and so on.

Model-based robot controllers require customized control-oriented models, involving expert knowledge and trial and error. Remarkably, the Koopman operator enables the control-oriented model identification through the input–output mapping set, breaking through the barriers of the customization services. However, in recent years, research on Koopman-based robot control has mostly focused on lifting function construction, deviating from the original intention of improving the controller performance. Thus, we propose a robot controller autogeneration framework using the Bayesian-based Koopman operator, significantly releasing labor and eliminating the design obstacle. First, we introduce the Koopman-based system identification method and offer the basic lifting function design criteria. Then, a Bayesian-based optimization strategy with resource allocation is designed, which allows for the simultaneous optimization of the lifting function and the controller. Next, taking model-predictive control (MPC) as an example, a mission-oriented controller autogeneration framework is developed. Simulation and experimental results indicate that, under various robots and data sources, the proposed framework can effectively generate the robot controllers and perform with a far greater level of mission accuracy than the unoptimized Koopman-based MPC. Meanwhile, the proposed technique exhibits an obvious compensation effect against disturbances, demonstrating its practicability in robot control.

As a key technology for autonomous underwater operations, precise tracking control in tight space environments is a great challenge. With the aid of high maneuverability of the underwater bionic robot, this article proposes an integrated tracking control framework for a robotic dolphin to move through narrow areas, including top-level planning, middle-level tracking, and bottom-level control allocation. First, a nonlinear model predictive control-based planning method is presented with full consideration of tracking accuracy and obstacle avoidance safety. Second, in order to improve the anti-interference ability, we derive a nonlinear path tracking control law by combining the backstepping technique with a nonlinear disturbance observer. More importantly, through hydrodynamic analysis of the bionic multimodal motions under flippers and flukes, a fuzzy-based nonlinear control allocation system is particularly adopted to convert calculated control forces into bionic motion parameters. Finally, extensive simulations and aquatic experiments are conducted, and the obtained results validate the effectiveness of proposed methods, providing a new idea to further ocean exploration.

The high speed and low energy cost are two conflicting objectives in the motion optimization of bio-inspired underwater robots, but playing a very important role. To this end, this paper proposes an optimization strategy for swimming speed and power cost using an improved NSGA-II for a flexible robotic fish. A dynamic model involving flexible deformation is established for speed prediction with the hydrodynamic parameters identified. A back propagation (BP) neural network is applied to perform compensation of power cost prediction with the dynamic model's prediction as input. In particular, an NSGA-II-AMS method is developed to improve the efficiency of solving the two-objective optimization problem based on NSGA-II. Finally, extensive simulations and experimental results demonstrate the effectiveness of the proposed optimization strategy, which offers promising prospects for the flexible robotic fish performing aquatic tasks with different performance constraints.

This article proposes a novel barrier-based adaptive line-of-sight (ALOS) three-dimensional (3-D) path-following system for an underactuated multijoint robotic fish. The framework of the developed path-following system is established based on a detailed dynamic model, including a barrier-based ALOS guidance strategy, three integrated inner-loop controllers, and a nonlinear disturbance observer (NDOB)-based sideslip angle compensation, which is employed to preserve a reliable tracking under a frequently varying sideslip angle of the robotic fish. First, a barrier-based convergence strategy is proposed to deal with probable along-track error disruption and suppress the error within a manageable range. Meanwhile, an improved adaptive guidance scheme is adopted with an appropriate look-ahead distance. Afterward, a novel NDOB-based sideslip angle compensation is put forward to identify the varying sideslip angle independent of speed estimation. Subsequently, inner-loop controllers are intended for regulation about the controlled references, including a super-twisting sliding-mode control (STSMC)-based speed controller, a global fast terminal sliding-mode control (GFTSMC)-based heading controller, and a GFTSMC-based depth controller. Finally, simulations and experiments with quantitative comparison in 3-D linear and helical path following are presented to verify the effectiveness and robustness of the proposed system. This path-following system provides a solid foundation for future marine autonomous cruising of the underwater multijoint robot.

This article proposes the mechanical design and dynamic model of an innovative manta-inspired robot system for both efficient fast swimming and high spatial maneuverability. Inspired by some biological studies, a pair of unique pectoral fins with six separate degrees of freedoms (DOFs) are developed. The novel design is characterized by an improved crank-rocker mechanism and a distinctive horizontal DOF. The former not only endows the robot with high swimming speed, but also guarantees efficient flapping patterns which are close to manta rays. The latter is employed to coordinate with the flapping movement, allowing remarkable pitch adjustment. Further, the basic motion strategy is presented by detailed analyses to the pectoral fins. Besides, based on the Morrison equation and infinitesimal method, a complete dynamic model for robotic manta with flexible pectoral fins is established, whose parameters are determined through experimental data. Moreover, the linear swimming and pitching experiments are conducted, demonstrating the prominent movement performance of the presented design and the effectiveness of the dynamic model. The obtained results shed light on updated design and control of next-generation agile underwater vehicles and robots capable of multimodal motions in dynamic and complex aquatic environments.

This paper proposes a multi-objective visionbased navigation network for biomimetic underwater robots to cope with scientific observation, target selection, and obstacle avoidance in marine missions. Structurally, a stereo block attention module is first constructed to serially extract the channel and spatial attention portion of the real-time visual feedback. Next, the parallax attention mechanism is introduced to enable the network to excavate implicit parallax information in stereo pairs, effectively eliminating the oscillation of the network output in the presence of ambiguous visual input. Further, with the assistance of other low-cost sensors, the proposed navigation network can be expanded in some largescale application scenarios, such as sparse coral observation. Finally, underwater simulations reveal that the proposed method obtains significantly improved control effect and real-time ability, compared with other related works. In particular, based on a self-developed biomimetic robotic dolphin, collision-free simulations with a cumulative distance beyond 1000 m were carried out and validated the effectiveness and the superiority of the navigation network, where both dense and sparse targets were fully tested. The robotic dolphin can not only successfully conduct accurate coral observation without collision, but also quest the observation targets as much as possible in the area where the observation targets are concentrated. The proposed network provides an intelligent and efficient navigation scheme for autonomous underwater operation of small-size underwater robots.

Underwater visual localization is an essential technique for the autonomous operation of underwater robots. However, the unique underwater image characteristics, including refraction, sparse features, and severe noise, pose an enormous challenge to it. For addressing these issues, this paper proposes an open-source fiducial-based underwater stereo visual-inertial localization method under the extended Kalman filter (EKF) framework, which is called FBUS-EKF. First, the refraction is corrected by the refractive camera model and akin triangulation. Second, the fiducial marker and a novel marker pose estimation method are applied to alleviate the adverse effect of sparse features. Third, the EKF is utilized to fuse the inertial and visual information so as to reject the serious noise. Finally, extensive experiments on a test bench demonstrate the effectiveness of the FBUS-EKF method, where the typical localization error is less than 3%, namely, the average error is lower than 3 cm within one meter. The obtained results reveal that the FBUS-EKF method has the prospect to be applied in the precise short-range operation and the localization for underwater robots, which offers a valuable insight for further autonomous underwater task.

This article presents the mechanical design and locomotion control of a novel tuna-inspired robot system for both fast swimming and high maneuverability. Mechanically, the developed robotic fish named CasiTuna comprises three important parts, i.e., an innovative two-motor-actuated propulsive mechanism, a buoyancy adjustment structure, and a pair of pectoral fins. Unlike most robotic fishes' multiple concatenated links-based propulsive mechanism, CasiTuna's two-motor-actuated one places both motors in the anterior body and utilizes a transmission system to achieve tuna-like lateral undulations. Meanwhile, the buoyancy adjustment mechanism in conjunction with pectoral fins endows the robot with the capability of three-dimensional maneuverability. Kinematic and dynamic analyses are further conducted to reveal the interactive hydrodynamic forces. Regarding the locomotion control method, a bio-inspired central pattern generator-based controller is adopted to achieve multimodal swimming. In particular, two kinds of turning maneuvers are implemented and discussed. Aquatic experiments, including straight swimming, circular turning, and nearly static pitching validate the effectiveness of proposed mechatronic design and locomotion control methods. Remarkably, CasiTuna achieved a peak forward speed of 0.8 m/s (corresponding to 1.52 body lengths per second) and a minimum turning radius of less than 0.3 body lengths.

This paper presents a three-dimensional (3-D) path planning method that combines the gliding with dolphin-like motions for the gliding robotic dolphin. A specific task that the robot uses the gliding motion for long-distance cruise and the dolphin-like motion for maneuverable obstacle avoidance is employed. The results of simulations and aquatic experiments validate the full-state dynamic model and the specific task, further offer some theoretical supports for 3-D path planning. Further, the 3-D path planning method is composed of three main components: 1) gliding path generation; 2) improved Astar (A*) algorithm; and 3) segmented Bezier curve smoothing. First, the gliding path is generated autonomously with the kinematic constraints that are obtained via the simulations of dynamic model. Furthermore, when the obstacles are detected by the sonar, an improved A* algorithm is employed to avoid the obstacles. Afterward, considering the path planned by A* is unsmoothed, a segmented Bezier curve method is presented. Simulation results demonstrate the effectiveness of the method, offering valuable insight into the utilization of hybrid underwater robots in the context of real-time task execution.

This article proposes a model predictive control (MPC)-based depth control system for the gliding motion of a gliding robotic dolphin. An injector-based buoyancy-driven mechanism is employed to achieve more precise control of net buoyancy. In the system, a novel framework of depth control is proposed on the basis of a simplified model, including a depth controller with improved MPC, a heading controller with velocity-based proportional-integral-derivative, and a sliding mode observer. Extensive simulation and experimental results demonstrate the effectiveness of the proposed control methods. In particular, a variety of slider-based experiments are also conducted to explore the performance of a movable slider in the depth control so as to better govern the gliding angle. The results obtained reveal that it is feasible to realize regular gliding angles via regulating the slider, which offers promising prospects for bio-inspired gliding robots playing a key role in ocean exploration.