To solve the problem of ship heave motion in harsh marine environments, which affects the positioning accuracy and safety of its robotic arm, this paper adopts a PID parameter optimization method based on Tent cold light source improved sparrow search algorithm. Firstly, utilizing Tent chaotic mapping to enhance the diversity of sparrow populations, and inspired by the fluorescence attraction effect in the firefly algorithm, this paper perturbs sparrows with a cold light source strategy to obtain better positions and improve their search performance. Secondly, simulation experiments were conducted to optimize the PID control system using improved algorithms. The results showed that compared with the three population intelligent optimization algorithm, the improved algorithmhad better convergence accuracy, system response speed, and stability. Finally, the improved algorithmwas applied to the actual experiment of compensating for the heave direction at the end of a shipborne robotic arm. The comparison of the four algorithms in compensating for the height difference in the heave direction showed that the improved algorithm had the best heave compensation effect, reflecting the effectiveness of the algorithm in engineering applications. This provides a certain reference for the active compensation of swarm intelligence optimization algorithms in the heave direction of ships.
KEYWORDS: Point clouds, Semantics, Fiber optic gyroscopes, Adverse weather, Data modeling, LIDAR, Denoising, Education and training, 3D modeling, Image segmentation
The accurate 3D comprehension of point cloud scenes in diverse weather conditions holds paramount significance in various applications such as autonomous driving in contemporary automobiles, outdoor operations of robots, and autonomous drones. Presently, the majority of studies on semantic segmentation algorithms for 3D point clouds primarily focus on clear weather conditions. However, adverse weather conditions introduce specific types of noise that significantly deteriorate the quality of point clouds. Consequently, this poses a challenge in achieving high accuracy and efficiency in point cloud semantic segmentation for outdoor large-scale scenarios. To tackle this issue, this paper presents a novel semantic segmentation method designed for large scenes encompassing point cloud and foggy weather conditions. We further validate our approach using the Foggy Semantickitti dataset, thereby effectively enhancing the average cross-parallel ratio while maintaining computational efficiency.
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