基于计算机视觉和激光雷达的端口通道的自主导航和碰撞预测
Zhan Zhang1, NanWu Yang2, YiJian Yang2
1Southwest Research Institute for Hydraulic and Water Transport Engineering, Chongqing Jiaotong University, Chongqing, 400016, China. 990020030767@cqjtu.edu.cn.
Scientific reports
|May 17, 2024
概括
本研究介绍了一种增强的算法,用于更安全,更有效的港口导航. 改进的系统大大降低了船只碰撞的风险,并优化了导航路径,从而提高了港口吞吐量.
科学领域:
- 海上安全的航行.
- 机器人技术和自主系统
- 计算机视觉 计算机视觉
背景情况:
- 港口航行面临着船只碰撞和环境风险带来的挑战.
- 优化水路利用对于增加港口吞吐量至关重要.
- 准确的环境意识和障碍物检测对于安全航行至关重要.
研究的目的:
- 提高港口航行安全和效率.
- 为了减少船只碰撞事故和环境风险.
- 为了优化水路使用和增加港口吞吐量.
主要方法:
- 使用激光雷达扫描和点云数据生成了一个3D港口水路地图.
- 使用视觉感知,用于对象检测的单射击多盒探测器 (SSD) 和人工潜力场方法来增强快速探索随机树 (RRT) 路径规划.
- 利用K-Means集群来改进用于碰撞预测的更快R-CNN算法.
主要成果:
- 改进的RRT将平均路径长度减少了14%,平均时间消耗减少了27%.
- 最大碰撞风险从15%降至8%.最大碰撞风险从15%降至8%.
- 对于碰撞预测,K-Means + Faster R-CNN模型实现了92%的准确性,88%的回忆率和90%的F1得分.
结论:
- 提议的增强算法为在港口水路上的自主导航提供了显著的优势.
- 综合方法提高了路径规划和碰撞预测准确度.
- 这项技术有助于实现更安全,更有效的海上运营.
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