The issue of controlling a swarm of autonomous unmanned surface vehicles (USVs) in a practical maritime environment is studied in this paper. A hierarchical control framework associated with control algorithms for the USV swarm is proposed. In order to implement the distributed control of the autonomous swarm, the control framework is divided into three task layers. The first layer is the tele-operated task layer, which delivers the human operator’s command to the remote USV swarm. The second layer deals with autonomous tasks (i.e. swarm dispersion, or avoidance of obstacles and/or inner-USV collisions), which are defined by specific mathematical functions. The third layer is the control allocation layer, in which the control inputs are designed by applying the sliding mode control method. The motion controller is proved asymptotically stable by using the Lyapunov method. Numerical simulation of USV swarm motion is used to verify the effectiveness of the control framework.

For deep sea equipment suspension installation used in marine engineering, the multi-camera video motion analysis method is used to calculate the three-dimensional underwater trajectory of the underwater engineering structure. Considering difficulty in underwater modeling caused by the problems of light scattering and refraction under water, the camera imaging model on land is no longer applicable in water, and a new underwater camera imaging model needs to be proposed. This paper introduces an underwater camera imaging model with light refraction, studies the calibration method of the internal and external parameters of the underwater camera, and improves the multiscale rotation dense feature pyramid convolutional neural network to detect the position of the target object in the image. The underwater motion videos of the target produced by three fixed underwater cameras are optimized to fuse and calculate the trajectory of the underwater target. This method is suitable for large-scale motion of underwater objects and can obtain more accurate trajectories. Experimental analysis and data comparison have verified the effectiveness of the method.

The long endurance Autonomous Underwater Vehicle (AUV) could finish autonomous mission process without any human aids in a long time. As an artificial intelligence system, AUV has high independence, reliability and adaptability to ocean environment. An efficient architecture of AUV plays an important roll in achieving those properties. A long endurance AUV, which is used for ocean exploring, was introduced. And its motion control system architecture was described. The architecture is divided into four parts including blackboard system, elementary behavior agent group, reflection behavior agent group and execution agent. Both the hardware and software in the control system were described. Finally, the experiments in tank and those at sea were conducted to test the architecture and the methods of the control system, and the results proved that the control system of the AUV is reliable, flexible and extensible.