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智能ROUV的进步为在苏伊士运河增强的导航

Khaled Oqda¹, Kareem Mosaad Ibrahim², Ahmed Ibrahim Mustafa²

¹Mechatronics Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt. khaledoqda@std.mans.edu.eg.

Scientific reports

|November 7, 2025

概括

本研究介绍了3Clifs,这是一款使用LiDAR进行狭窄水道的精确地形绘图的AI增强的远程操作水下车辆 (ROUV). 该系统在具有挑战性的水下环境中改善了导航和数据完整性.

科学领域:

机器人技术和自主系统
地理空间科学与技术
海洋工程海洋工程

背景情况:

远程操作的水下车辆 (ROUV) 对于高分辨率测量受限水生环境至关重要.
水下LiDAR数据采集面临着由于散射和反射造成的挑战,影响绘图准确度.
同时定位和映射 (SLAM) 对于ROUV导航至关重要,但可能会因数据丢失而退化.

研究的目的:

开发和演示一个人工智能增强的ROUV系统 (3Clifs),用于近乎实时的地形绘图,在浅,受限制的水道.
通过解决数据丢失,提高基于LiDAR的水下SLAM的连续性和完整性.
在狭窄的航道中增强ROUV导航支持.

主要方法:

集成3D LiDAR,惯性测量单元 (IMU) 和机器人操作系统 (ROS) 用于水下SLAM.
开发一个人工智能驱动的模块,用于重建丢失的LiDAR点云数据.
优化ROUV推进和螺旋设计 (螺旋v05_1) 以尽量减少流动流和提高扫描稳定性.

主要成果:

3Clifs系统展示了有效的LiDAR和AI集成,用于水下SLAM与缺失点恢复.
推进装置的修改显著提高了扫描稳定性,并减少了数据差距.
对比分析显示,优越的点云完整性和SLAM连续性高于仅使用声纳的方法.

结论:

集成的LiDAR,ROS和AI管道为在具有挑战性的条件下水下SLAM提供了强大的解决方案.
优化的推进增强了基于LiDAR的ROUV绘图的可靠性.
3Clifs系统在狭窄的苏伊士运河航道中为导航和绘制地图提供了实际的好处.

关键词:

人工智能的人工智能是人工智能.光探测和距离传感器传感器机器学习是机器学习.狭窄车道导航狭窄车道导航远程操作的水下车辆机器人操作系统操作系统无线控制系统的无线控制系统

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