船岸协同支持下的内河船舶远程驾控系统关键技术研究  

Key technologies of ship remote control system in inland waterways under ship-shore cooperation conditions

作　　者：马枫[1,3,5] 陈晨 刘佳仑[1,3] 王绪明 严新平[1,3,5] MA Feng;CHEN Chen;LIU Jialun;WANG Xuming;YAN Xinping(Intelligent Transport System Research Center,Wuhan University of Technology,Wuhan 430063,China;School of Computer Science and Technology,Wuhan Institute of Technology,Wuhan 430205,China;National Engineering Research Center for Water Transport Safety,Wuhan University of Technology,Wuhan 430063,China;Smart Waterway Co.,LTD,Nanjing 210028,China;East Lake Laboratory(ELL),Wuhan 420202,China)

机构地区：[1]武汉理工大学智能交通系统研究中心,湖北武汉430063 [2]武汉工程大学计算机科学与工程学院,湖北武汉430205 [3]武汉工程大学国家水运安全工程技术研究中心,湖北武汉430063 [4]南京智慧水运科技有限公司,江苏南京210028 [5]湖北东湖实验室,湖北武汉420202

出　　处：《中国舰船研究》2022年第5期125-133,共9页Chinese Journal of Ship Research

基　　金：国家自然科学基金资助项目(52171352);国家重点研发计划资助项目(2021YFB1600404);湖北省教育厅科学技术研究计划青年人才资助项目(Q20211502)。

摘　　要：[目的]面向弯曲、狭窄、拥挤内河水道,提出一种船岸协同支持下基于CNN算法和知识模型的船舶远程驾控方法。[方法]在剖析船岸协同特点的基础上,以视觉模拟为核心实现环境自主感知,以深度强化学习为基础实现航行决策控制,构造由图像深度学习处理、航行态势认知、航线稳态控制等功能组成的人工智能系统。实现内河条件下运营船舶的远程控制与短时自主航行,开展内河集装箱船、渡船的远程驾控示范。[结果]示范航行中,系统可依据远程或船上指令替代人工控制船舶,控制循线误差小于20 m,并可自主避障。[结论]研究证实,通过卷积神经网络、强化学习、知识模型协作建立的人工智能系统,可自主提取关键航行信息、构造避障与控制意识,部分替代船员的工作,可为内河智能航运的进一步发展奠定基础。[ Objective] To meet the requirements of remotely controlling ship in curved, narrow and crowded inland waterways, this paper proposes an approach that consists of CNN-based algorithms and knowledge based models under ship-shore cooperation conditions. [Method]On the basis of analyzing the characteristics of ship-shore cooperation, the proposed approach realizes autonomous perception of the environment with visual simulation at the core and navigation decision-making control based on deep reinforcement learning, and finally constructs an artificial intelligence system composed of image deep learning processing,navigation situation cognition, route steady-state control and other functions. Remote control and short-time autonomous navigation of operating ships are realized under inland waterway conditions, and remote control of container ships and ferries is carried out. [Results]The proposed approach is capable of replacing manual work by remote orders or independent decision-making, as well as realizing independent obstacle avoidance,with a consistent deviation of less than 20 meters. [Conclusions]The developed prototype system carries out the remote control operation demonstration of the above ship types in such waterways as the Changhu Canal Shenzhou line and the Yangtze River, proving that a complete set of algorithms with a CNN and reinforcement learning at the core can independently extract key navigation information, construct obstacle avoidance and control awareness, and lay the foundation for inland river intelligent navigation systems.

关 键 词：远程驾驶 智能船舶 自主航行 深度强化学习 船岸协同 

分 类 号：U675.73[交通运输工程—船舶及航道工程]