Chances and Challenges: Transformation from a Laser-Based to a Camera-Based Container Crane Automation System

被引:4
|
作者
Benkert, Johannes [1 ]
Maack, Robert [1 ]
Meisen, Tobias [1 ]
机构
[1] Univ Wuppertal, Chair Technol & Management Digital Transformat TMD, Rainer Gruenter Str 2, D-42119 Wuppertal, Germany
关键词
container handling; harbor; machine learning; deep learning; camera;
D O I
10.3390/jmse11091718
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
In recent years, a steady increase in maritime business and annual container throughput has been recorded. To meet this growing demand, terminal operators worldwide are turning to automated container handling. For the automated operation of a crane, a reliable capture of the environment is required. In current state-of-the-art applications this is mostly achieved with light detection and ranging (LiDAR) sensors. These sensors enable precise three-dimensional sampling of the surroundings, even at great distances. However, the use of LiDAR sensors has a number of disadvantages, such as high acquisition costs and limited mounting positions. This raises the question of whether the LiDAR systems of automated container terminals (ACT) can be replaced with cameras. However, this transformation is not easy to accomplish and is explored in more depth in this paper. The field of camera-based container automation presented in this publication is largely unexplored. To the best of our knowledge, there is currently no automated container terminal in real-world operation that exclusively uses cameras. This publication aims to create a basis for further scientific research towards the goal of a fully camera-based container automation. Therefore, the authors present a narrative review providing a broad overview of the mentioned transformation, identifying research gaps, and suggesting areas for future research. In order to achieve this, this publication examines the fundamentals of an automated container terminal, the existing automation solutions and sensor technologies, as well as the opportunities and challenges of a transformation from LiDAR to camera.
引用
收藏
页数:20
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