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When considering the working conditions under which an unmanned surface vehicle (USV) operates, the navigational sensors, which already have inherent uncertainties, are subjected to environment influences that can affect the accuracy, security and reliability of USV navigation. To combat this, multi-sensor data fusion algorithms will be developed in this paper to deal with the raw sensor measurements from three kinds of commonly used sensors and calculate improved navigational data for USV operation in a practical environment. Unscented Kalman Filter, as an advanced filtering technology dedicated to dealing with non-linear systems, has been adopted as the underlying algorithm with the performance validated within various computer-based simulations where practical, dynamic navigational influences, such as ocean currents, provide force against the vessel's structure, are to be considered. [ABSTRACT FROM AUTHOR]

In the past decade, there is an increasing interest in the deployment of unmanned surface vehicles (USVs) for undertaking ocean missions in dynamic, complex maritime environments. The success of these missions largely relies on motion planning algorithms that can generate optimal navigational trajectories to guide a USV. Apart from minimising the distance of a path, when deployed a USVs’ motion planning algorithms also need to consider other constraints such as energy consumption, the affected of ocean currents as well as the fast collision avoidance capability. In this paper, we propose a new algorithm named anisotropic GPMP2 to revolutionise motion planning for USVs based upon the fundamentals of GP (Gaussian process) motion planning (GPMP, or its updated version GPMP2). Firstly, we integrated the anisotropy into GPMP2 to make the generated trajectories follow ocean currents where necessary to reduce energy consumption on resisting ocean currents. Secondly, to further improve the computational speed and trajectory quality, a dynamic fast GP interpolation is integrated in the algorithm. Finally, the new algorithm has been validated on a WAM-V 20 USV in a ROS environment to show the practicability of anisotropic GPMP2. Note to Practitioners—The work reported in this article will be significant for USVs to conduct missions in complex, dynamic maritime environments where various obstacles and time-varying ocean currents exit. We develop this novel motion planning algorithm based on Gaussian process and optimise the trajectory using probabilistic inferences. The new algorithm can generate collision free trajectories that also minimise the influences caused by adverse ocean currents in a highly efficient way. In addition, the planning has been undertaken in a continuous-time domain making the generated trajectory have a guaranteed smoothness and readily feasible for autopilots to track. We use a coastal area with time-varying vortexes to present a challenging practical maritime environment. The presented algorithm integrates the available information about a fluid field regarding energy consumption and hazard level, along with the density of obstacles to plan a navigational route efficiently. To increase the practical performance of the proposed method, diverse models for generating ocean currents need to be developed in the future to tackle unpredictable situations. [ABSTRACT FROM AUTHOR]

With an increasing interest in the deployment of unmanned surface vehicles (USVs) to support complex ocean operations, high‐speed USVs (≥40 knots) have become an important option, especially in accomplishing demanding tasks such as coastal guarding. At present, there is a vast amount of studies focusing on the development of USVs' autonomous navigation systems, and the results of most of them have only been verified by simulations or demonstrations on low‐speed experimental USVs (≤20 knots). It remains unclear how high‐speed USVs would perform in real applications. In particular, when USVs are navigating at high‐speed, reliable obstacle detections and robust motion control become increasingly difficult to achieve than in normal conditions. This paper therefore presents a new autonomous navigation system for high‐speed USVs with the system software and hardware been designed on the basis for ensuring a safe and reliable operation. Within such a system, a collision avoidance algorithm based on velocity obstacle able to adjust the USV's heading and speed in real‐time has been integrated. A local environment modelling method and a heading maintain method by considering Coast Guard International Regulations for Avoiding Collision at Sea are proposed to improve the reliability and stability of the system. The effectiveness and efficiency of the proposed system has been verified and validated by hardware‐in‐the‐loop simulation tests and sea trials on a high‐speed USV ("Tianxing No. 1") in practical maritime environments. Results show that the autonomous navigation system adapts to the characteristics of high‐speed USVs and can guide high‐speed USVs (≥40 knots) in realising a safe autonomous navigation, which significantly improves the USV's autonomy level. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) have obtained increasing interests in recent decades. Because of the features of improved mission efficiency and decreased resource costs, applications of USVs can been seen in both civilian and naval areas. In order to efficiently and effectively achieve missions without any human intervention, a robust and intelligent navigation, guidance and control (NGC) system is vital for USV. This paper has therefore presented a novel NGC system designed for a USV named Springer . The system is developed by integrating multiple functional modules, which include a reliable navigation module that provides reliable position and heading information, a robust autopilot module enabling Springer tracking well the waypoints and an intelligent path planning module that is capable of generating feasible and practical waypoints. The path planning algorithm has been developed based upon the angle guidance fast marching square method, which is able to calculate the optimal path according to vehicle's motion constraints. The designed NGC system has been validated in both real field trials and computer based simulations proving that Springer USV is able to autonomously navigate in different maritime environments with the guidance of the NGC system. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) are receiving increasing attention in recent years from both academia and industry. To make a high-level autonomy for USVs, the environment situational awareness is a key capability. However, due to the richness of the features in marine environments, as well as the complexity of the environment influenced by sun glare and sea fog, the development of a reliable situational awareness system remains a challenging problem that requires further studies. This paper, therefore, proposes a new deep semantic segmentation model together with a Simple Linear Iterative Clustering (SLIC) algorithm, for an accurate perception for various maritime environments. More specifically, powered by the SLIC algorithm, the new segmentation model can achieve refined results around obstacle edges and improved accuracy for water surface obstacle segmentation. The overall structure of the new model employs an encoder–decoder layout, and a superpixel refinement is embedded before final outputs. Three publicly available maritime image datasets are used in this paper to train and validate the segmentation model. The final output demonstrates that the proposed model can provide accurate results for obstacle segmentation. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) are new marine intelligent platforms that can autonomously operate in various ocean environments with intelligent decision-making capability. As one of key technologies enabling such a capability, path planning algorithms underpin the navigation and motion control of USVs by providing optimized navigational trajectories. To accommodate complex maritime environments that include various static/moving obstacles, it is important to develop a computational efficient path planning algorithm for USVs so that real-time operation can be effectively carried out. This paper therefore proposes a new algorithm based on the fast sweeping method, named the locking sweeping method (LSM). Compared with other conventional path planning algorithms, the proposed LSM has an improved computational efficiency and can be well applied in dynamic environments that have multiple moving obstacles. When generating an optimal collision-free path, moving obstacles are modelled with ship domains that are calculated based upon ships' velocities. To evaluate the effectiveness of the algorithm, particularly the capacity in dealing with practical environments, three different sets of simulations were undertaken in environments built using electronic nautical charts (ENCs). Results show that the proposed algorithm can effectively cope with complex maritime traffic scenarios by generating smooth and safe trajectories. [ABSTRACT FROM AUTHOR]

This paper investigates the path-guided coordinated control (PACC) of unmanned surface vehicles (USVs) under communication interruption. All USVs follow the single-path-guided control method and only certain USVs have access to the virtual leader. In order to maintain the USVs formation to the maximum extent and avoid individual losses when avoiding the communication shielding area, a fixed-time formation control method based on exponential smoothing prediction is proposed in this paper. Firstly, a sigmoid function based artificial potential field is designed to optimize the kinematics control output of USVs during obstacle avoidance. Secondly, a fixed-time distributed kinematics control law based on Holt-winters exponential smoothing (HWES) prediction is developed to maintain the USVs formation to the maximum extent under communication interruption. Finally, a fixed-time cascade control system is proposed to achieve fast convergence of kinematic and kinetics errors. Numerical simulations and comparisons are conducted to demonstrate the effectiveness and advantages of the proposed method. • A sigmoid function based artificial potential function is proposed to ensure USVs avoid obstacles smoothly and avoid kinematic input saturation. • A data-driven predictor based on Holt-winters exponential smoothing is designed to maintain USVs formation under communication interruption. • A fixed-time cascade control system is proposed. In kinematic layer, a fixed-time distributed kinematic control law is developed by combining a fixed-time sideslip angle observer. In kinetic layer, a CONN based fixed-time sliding mode controller is developed. [ABSTRACT FROM AUTHOR]

In recent decades, unmanned surface vehicles (USVs) are attracting increasing attention due to their underlying capability in autonomously undertaking complex maritime tasks in constrained environments. However, the autonomy level of USVs is still limited, especially when being deployed to conduct multiple tasks simultaneously. This paper, therefore, aims to improve USVs autonomy level by investigating and developing an effective and efficient task management algorithm for multi-USV systems. To better deal with challenging requirements such as allocating vast tasks in cluttered environments, the task management has been de-composed into two submissions, i.e., task allocation and task execution. More specifically, unsupervised learning strategies have been used with an improved K-means algorithm proposed to first assign different tasks for a multi-USV system then a self-organising map (SOM) been implemented to deal with the task execution problem based upon the assigned tasks for each USV. Differing to other work, the communication problem that is crucial for USVs in a constrained environment has been specifically resolved by designing a new competition strategy for K-means algorithm. Key factors that will influence the communication capability in practical applications have been taken into account. A holistic task management architecture has been designed by integrating both the task allocation and task execution algorithms, and a number of simulations in both simulated and practical maritime environments have been carried out to validate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]