Artificial intelligence has become a new engine to achieve innovation-driven development. Integrating artificial intelligence and education will lead to a new education development model and promote better education. Analysing the development trends in artificial intelligence education in various countries shows the demands for training and platform construction of artificial intelligence education. Artificial intelligence education presents the characteristics of diversified technology, personalized instruction, and intelligent student assessment.
This study focuses on the research of spray-pump type unmanned surface vehicles (USVs). Based on the analysis of the dynamics and motion characteristics of USVs, a method combining Dubins curves and the particle swarm optimization algorithm is proposed to find the optimal or suboptimal solution for the formation path. The research goal is to switch multiple USVs from an unordered state to a formation state, taking the speed of USVs and the formation endpoint as dynamic variables and integrating Dubins curve theory into the particle swarm optimization algorithm. A theoretical framework for the formation of spray-pump type USVs is proposed. Finally, the formation of three USVs is realized in a simulation platform.
Infrared ship target recognition technology can automatically detect, analyze and identify ship targets, which is suitable for various types of working environments. This paper takes ship target image as the research object, and measures the effect of existing infrared detection technology, traditional target detection technology and target detection technology based on depth learning through data comparison. For infrared detection and target recognition, its working principle is summarized, and its ability to quickly identify ship types in practical applications is verified. The application and development status of Sea area planning、 supervision and Military reconnaissance field are summarized, and the future development trend is prospected
Aiming at the situation that small unmanned surface vehicle (USV) encounters unknown disturbance during low speed sailing, a course controller with finite time stability is designed. To solve this problem, we construct an undisturbed ideal navigation model which simply meets the stability requirements, and constructs an adaptive sliding mode surface. The control under finite time approach law is also introduced. The model under perturbation can land on the sliding mode surface in finite time and then synchronize with the ideal navigation model. The adaptive control was applied in the implementation of power control for the thruster structure, so as to ensure the tracking of the desired course within the finite time, and satisfy the needs for the stable system performance. Lyapunov direct method is used to strictly prove that the designed controller can ensure the system which converges to the steady state value in a given time period. Simulation results show that the designed adaptive finite-time controller can ensure the stable course tracking of the USV with thruster structure at low speed, and meet the requirements of the course robustness of the USV under dynamic conditions.
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