ROV水下障碍物检测和避障技术的应用综述  

Comprehensive Review of ROV Underwater Obstacle Detection and Avoidance Technology

作　　者：李明桂 周焕银[1] 龚利文 LI Minggui;ZHOU Huanyin;GONG Liwen(School of Mechanical and Electronic Engineering,East China University of Technology,Nanchang 330000,China)

机构地区：[1]东华理工大学机械与电子工程学院,南昌330000

出　　处：《计算机工程与应用》2024年第17期34-47,共14页Computer Engineering and Applications

基　　金：国家自然科学基金(62063001);江西省科技厅重点基金(20224ACB204022)。

摘　　要：全面回顾了远程操作车(ROV)在水下障碍物检测和避障技术方面的技术进展。研究集中于声呐系统、光学系统及其与机器学习和人工智能算法的结合,分析了这些技术如何提高水下作业的自主性、效率和安全性。尽管声纳和光学系统在环境适应性和障碍物检测精度方面已取得显著成果,但动态障碍物实时识别和复杂环境适应性的挑战仍待克服。此外,探讨了机器学习和人工智能技术在增强ROV自主避障能力方面的潜力和挑战,指出了这些技术在未来ROV操作中的重要性。该研究为深海探索和海洋科学提供了新的理论视角和应用实践。This paper provides a comprehensive review of the technological advancements in underwater obstacle detec-tion and avoidance techniques for remotely operated vehicles(ROV).The research focuses on sonar systems,optical sys-tems,and their integration with machine learning and artificial intelligence algorithms,analyzing how these technologies enhance the autonomy,efficiency,and safety of underwater operations.Despite significant achievements in environmental adaptability and obstacle detection accuracy achieved by sonar and optical systems,challenges remain in real-time identifi-cation of dynamic obstacles and adaptation to complex environments.Furthermore,the potential and challenges of machine learning and artificial intelligence technologies in enhancing ROV’s autonomous obstacle avoidance capability are dis-cussed,highlighting the importance of these technologies in future ROV operations.This research provides new theoret-ical perspectives and practical applications for deep-sea exploration and marine science.

关 键 词：水下障碍物检测 自主避障 声纳系统 光学系统 机器学习与人工智能 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]