A marine autonomous surface vehicle (ASV) is a kind of autonomous marine robot with intelligent and flexible use advantages. They are mainly divided into two categories: unmanned vessels and unmanned sailboats. Marine ASVs are essential in marine science, industry, environmental protection, and national defense. One of the primary challenges faced by marine ASVs is autonomously planning paths in an intricate marine environment. Numerous research findings have surfaced in recent years, including the combination with popular machine learning. However, a systematic literature review is still lacking, primarily a comprehensive comparison of two types of ASV path planning methods. This review first introduces the problem and evaluation indicators of path planning for ASVs. Then, aiming at unmanned vessels and sailboats, respectively, it sorts out various path planning algorithms proposed in the existing literature, including the advantages and limitations of both kinds of ASVs, and discusses them in combination with evaluation indicators. Also, this paper explores how marine environmental factors affect path planning and its corresponding treatment methods. Finally, this review summarizes the challenges of unmanned ship path planning, proposes potential technical solutions and future development directions, and aims to provide references for further development in this field.

The cooperative motion of multiple mobile robots has attracted wide attention due to its advantages in military, marine and aerospace fields, and formation control has become a significant technology in the realization of these tasks. However, most of the existing formation control designs of mobile robots do not consider the practical obstacles in the environment, and the maintenance of both formation and trajectory tracking while confronting the obstacles is still a challenging issue. Therefore, in this paper, a virtual-structure-based formation control approach is designed with obstacle avoidance for a system with multiple mobile robots. The basic trajectory is generated for each robot in the group and parameterized to keep the group in formation. A trajectory generator is then established regarding the obstacles, where a potential function is designed to adjust the basic trajectory and replan the reference trajectory to achieve obstacle avoidance. Then, a novel design for the path parameter is proposed to improve the performance of the robot group when encountering obstacles. Finally, a tracking controller is designed to achieve good tracking performance for robots, and the guaranteed performance is achieved via the Lyapunov theorem. A comparative simulation with three sets is carried out, where an objective function $F_{obj}$ is designed to evaluate the tracking performance in the presence of obstacles. Besides this, a real experiment is implemented to further verify the effectiveness. The simulation and experimental results verify the good formation and tracking performance of the proposed design for a system with multiple mobile robots with obstacle avoidance.

Hypoxia is becoming a serious problem in coastal waters in many parts of the world. Artificial downwelling, which is one of the geoengineering-based adaptation options, was suggested as an effective means of mitigating hypoxia in coastal waters. Artificial downwelling powered by green energy, such as solar, wind, wave, or tidal energy, can develop a compensatory downward flow on a kilometer scale, which favors below-pycnocline ventilation and thus mitigates hypoxia in bottom water. In this paper, we review and assess the technical, numerical, and experimental aspects of artificial downwelling all over the world, as well as its potential environmental effects. Some basic principles are presented, and assessment and advice are provided for each category. Some suggestions for further field-based research on artificial downwelling, especially for long-term field research, are also given.

In the traditional motion control method of an unmanned sailboat, the sail and rudder are divided into two independent controllers. The sail is used to obtain the thrust and the rudder is used to adjust the yaw angle. The traditional control method does not consider the synergy between the two controllers and ignores the influence of the roll angle on sailing. It is easy for these methods to cause an excessive roll angle and large yaw angle error, which will weaken the safe navigation and accurate path tracking of an unmanned sailboat. This paper presents a collaborative control method of sail and rudder based on model predictive control. A four-degree-of-freedom kinematics and dynamics model of the unmanned sailboat considering roll angle was established, with the yaw angle and roll angle as the control objectives at the same time. The collaborative control method outputs sail angle and rudder angle simultaneously. By comparing the motion of this method and the separation control of sail and rudder under the same wind field conditions, it is verified that the collaborative control has better effects of yaw angle control and roll angle limitation and can obtain a more accurate path tracking effect.