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.

Multiobjective optimization is a common problem in the field of industrial cutting. In actual production settings, it is necessary to rely on the experience of skilled workers to achieve multiobjective collaborative optimization. The process of industrial intelligence is to perceive the parameters of a cut object through sensors and use machines instead of manual decision making. However, the traditional sequential algorithm cannot satisfy multiobjective optimization problems. This paper studies the multiobjective optimization problem of irregular objects in the field of aquatic product processing and uses the information guidance strategy to develop a simulated annealing algorithm to solve the problem according to the characteristics of the object itself. By optimizing the mutation strategy, the ability of the simulated annealing algorithm to jump out of the local optimal solution is improved. The project team developed an experimental prototype to verify the algorithm. The experimental results show that compared with the traditional sequential algorithm method, the simulated degradation algorithm designed in this paper effectively improves the quality of the target solution and greatly enhances the economic value of the product by addressing the multiobjective optimization problem of squid cutting. At the end of the article, the cutting error is analyzed.

Sonar images have the characteristics of lower resolution and blurrier edges compared to optical images, which make the feature-matching method in underwater target tracking less robust. To solve this problem, we propose a particle filter (PF)-based underwater target-tracking method utilizing Zernike moment feature matching. Zernike moments are used to construct the feature-description vector for feature matching and contribute to the update of particle weights. In addition, the particle state transition method is optimized by using a first-order autoregressive model. In this paper, we compare Hu moments and Zernike moments, and we also compare whether to optimize the particle state transition on the tracking results or not based on the effects of each option. The experimental results based on the AUV (autonomous underwater vehicle) prove that the robustness and accuracy of this innovative method is better than the other combined methods mentioned in this paper.