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Most of the previous research about unmanned surface vehicle (USV) control system just focuses on reaching the target but not considers berthing. Moreover, computation cost is rarely concerned. For automatic target arriving and berthing of USV in presence of multi-shape convex obstacles and disturbances, this paper designs a composite control system including trajectory optimization and trajectory tracking. In order to generate a smooth and optimal trajectory, two objectives including minimum running time and energy consumption are proposed, and their priority requirement is presented. Fuzzy satisfactory optimization is introduced to reformulate the objective functions, and the membership degree difference is used to model priority between two objectives. The obstacle avoidance constraint is developed based on the improved P-criterion. The comprehensive optimization model is established to obtain the maximum membership degree. Further, the priority optimization model is proposed to expand the feasible domain by relaxing the comprehensive optimization result. Two models are discretized by Gaussian pseudospectral method to find the optimal solution. To achieve good trajectory tracking under disturbances, two first-order active disturbance rejection control (ADRC) controllers are designed to follow the desired longitudinal velocity and course angular velocity. All the velocities of USV can converge. The simulation proves the effectiveness of the proposed co-designed system by comparing with other methods.

In order to realize waypoint-based automatic berthing alongside or astern, a generation algorithm of dynamic virtual ships is proposed to guide the underactuated surface vehicle (USV). In this paper, a nonlinear model predictive control (NMPC) problem is set up with multiple shooting method, which lifts the system and improves convergency. Measuring error and disturbances are reduced by moving horizon estimation (MHE) where the weights of sensors are auto-tuned according to their variation. Simulations of berthing alongside and astern of ship BAY CLASS illustrates the position error is smaller than 0.6 m and course error is smaller than 0.5°. Compared to adaptive sliding mode control, proposed method has advantage on state smoothness and the control performance meets the requirements of berthing accuracy.

Maritime operations, such as ship hull inspections, infrastructure assessments, and rapid response to emergencies, often expose humans to hazardous environments. The deployment of unmanned vehicles offers a potential solution to mitigate risks and reduce operational costs in such scenarios. Collaborative efforts between Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) can enhance mission endurance and portability. This paper introduces a cooperative UAV-USV system designed to expand the operational coverage area. It leverages off-the-shelf satellite-based technology to establish robust communication links between UAVs, USVs, and the ground station. This connection facilitates the development of an autonomous launch and recovery system, relying on GPS and visual servoing. Furthermore, the system incorporates a Battery Hot Swap-system (BHS) on the USV, allowing the UAV to resume its mission within 3 minutes. The effectiveness of this proposed solution was demonstrated through successful inspection trials conducted at the port of Hamburg, Germany.

Water target recognition is a critical challenge for the perception technology of unmanned surface vessels (USVs). In the application of USV, detection accuracy and the inference time both matter, while it is tough to strike a balance and single-frame water target detection behaves unstable in the video detection. To solve these problems, many strategies are applied to increase YOLOv4’s performance, including network pruning, the focal loss function, blank label training, and preprocessing with histogram normalization. The optimized detection method achieves a mean average precision (mAP) of 81.74% and a prediction speed of 26.77 frames per second (FPS), which meets the USV navigation requirements. To build the integrated USV-based system for water target recognition, a water target dataset containing 9936 images is created from offshore USV experiments in which the human-in-the-loop annotation and mosaic data augmentation methods are used. The issues of miss detection and false alarm can be considerably mitigated by cascading the Siamese-RPN tracking network, and the major color of a water target can be retrieved using a local contrast saliency color detection scheme. The system being tested is called “ME120” includes an embedded edge computing platform (Nvidia Jetson AGX Xavier). Finally, online dataset learning demonstrates the improved YOLOv4 achieves an increase of 66.98% in FPS at the cost of a decrease of 0.79% in mAP when compared with the original YOLOv4 and offline navigation experiments validate that our system achieves high recognition capability while maintaining a high degree of robustness.

Unmanned Surface Vehicles (USVs) generate a large amount of data that needs to be processed in real time when they work, but they are usually limited by computational and battery resources, so they need to offload their tasks to the edge for processing. However, when numerous USVs offload their tasks to the edge nodes, some offloaded tasks may be thrown due to queuing timeouts. Existing task offloading methods generally consider the latency or the overall system energy consumption caused by the collaborative processing at the edge and end layers, and do not consider the wasted energy when the tasks are thrown. Therefore, to address the above situation, this paper establishes a task offloading model to minimize long-term task latency and energy consumption by jointly considering the requirments of latency and energy-sensitive tasks and the overall load dynamics in the cloud, edge, and end layers. A deep reinforcement learning (DRL)-based Task Offloading with Cloud Edge Jointly Load Balance Optimization algorithm (TOLBO) is proposed to select the best edge server or cloud server for offloading. Simulation results show that the algorithm can improve the utilization of energy consumption of the cloud edge nodes compared with other algorithms. At the same time, it can significantly reduce the task throw rate, average latency, and energy consumption of end devices.

This paper presents the control modelling and synthesis using a coupled multivariable under-actuated nonlinear adaptive U-model approach for an unmanned marine robotic platform. A nonlinear marine robotics model based on the dynamic equation using the Newtonian method and derivation with respect to the kinematics equations and rigid-body mass matrixes are explained. This nonlinear marine robotics model represents the underwater thruster dynamics, marine robotics dynamics and kinematics related to the earth-fixed frame. Coupled multivariable nonlinear adaptive control synthesis using a U-model approach for the Remotely Operated Vehicle (ROV) and Unmanned Surface Vessel (USV) represent an unmanned marine robotics application. A comparison is presented for the proposed nonlinear control approach between the U-model control approach with nonlinear Fuzzy Logic Control and Sliding Mode Control for the ROV and USV platforms. The results show minimum mean square error values and tracking performance between the plant or system model with the proposed method. Lastly, robustness and stability analysis for the proposed U-Model nonlinear control approach are presented by implementing an adaptive learning rate value.

A safety guaranteed formation control problem for waterjet unmanned surface vehicles (USV) under the consideration of unmodeled dynamics, environmental disturbances, input saturation, and output constraints is addressed in this paper. The leader-follower method and line-of-sight (LOS) strategy are employed to achieve the desired formation structure. Firstly, two tan-type barrier Lyapunov functions (BLF) are designed to limit the formation errors always within a predefined range to avoid collisions and keep communications, it can also relax the assumption of each USV's initial position to random. Besides, a bioinspired neurodynamics method is involved to avoid the appearance of the virtual velocity differential terms in the control law, which is different from the traditional dynamic surface method and makes it easier to implement in practice. Further, adaptive neural networks (NN) are used to approximate unmodeled dynamics and an auxiliary dynamics system is introduced to handle the input saturation. It is proved that the uniform ultimate boundedness of all signals in the control system can be achieved. Finally, simulation studies show the effectiveness of the proposed control law.

To satisfy the requirement of the future ocean communication and maritime transportation, in this paper, an integrated space-air-ground-sea communication control system based on mobile computing technology is proposed to rescue a water container. Specifically, unmanned surface vehicle (USV), as a flexible and lightweight intelligent device deployment integration platform, is the main tool to move towards the water container. However, how to sense the real-time navigation status information of the USV, integrate the information, and eventually distribute it to the ground control center, is still a difficult point for us. To successfully rescue the water container, three aspects of research are needed: information collection of the USV, information fusion, and data distribution to the ground control center. For the information collection part, we mainly use nine-axis, ultrasonic, global positioning system (GPS), and ultra wide band (UWB) sensors to complete the information collection for the USV. For the information fusion part, we set up a computational model to solve it. The USV computational model in previous works is mainly based on large ships, ignoring the modeling of wind, wave, and current for small underactuated USV. We use the classical Nomoto model, combined with the actual any slight wind, wave, and current forces on the USV, and finally combined with the proportion integration differentiation (PID) algorithm to model the USV. For the data distribution to the ground control center part, we have set up a communication protocol for the USV so that it can communicate normally. In addition, we wrote a Browser/Server (B/S) architecture to enable the ground control center to communicate with the host computer. Finally, our proposed control and communication schemes are carried out on actual USV. The real experiments show that the proposed schemes can reduce the ship surge and satisfy the ship navigation.

In the presence of the uncertain system dynamics, unknown time-varying disturbances and rudder saturation, this paper develops a novel robust adaptive course control scheme for unmanned surface vehicle (USV). Considering the characteristics of the rudder servo system, a double loop course controller of the practical and concise is proposed by the enhanced trajectory linearization control (TLC) technology. The key features of the developed controller are that, first, the neural networks are employed to online approximate unmodeled dynamics, and adaptive techniques are adopted to deal with completely unknown external disturbances; second, auxiliary systems that are governed by smooth switching functions, are developed in an unprecedented manner to compensate for the saturation constraints on actuators. The main innovation can be summarized as that the TLC technology is applied to the USV motion control field as a new control algorithm, and the enhanced technology based on traditional TLC not only reduces the number of adjustment parameters but also has simple structure and high robustness. Furthermore, a low frequency learning method improves the applicability of the algorithm. The stability analysis is established using the Lyapunov theory. Simulation results and comparison verify the effectiveness of the proposed strategy.

In this paper, a practical adaptive online constructive fuzzy control (AOCFC) algorithm is proposed to handle the trajectory tracking problem for the unmanned surface vessel under unknown time-varying uncertainties. In the AOCFC algorithm, the dynamic surface control technology combined with “logical virtual vessel”can program the rational reference route while avoiding the problem of “complexity explosion.”Besides, the proposed online constructive fuzzy approximator (OCFA) is devised to deal with the unknown time-varying uncertainties. The OCFA has two advantages: first, it can estimate the uncertainties without exact information of the dynamic model and external environmental disturbances; second, it employs decoupled distance measure and structure learning mechanism to dynamically and parsimoniously self-constructing fuzzy rules. At the same time, we have proved that the tracking errors of the closed-loop control system are uniformly ultimately bounded. Finally, the simulation result and comprehensive comparison demonstrate the proposed controller's performance and effectiveness.