Container Bay Position Detection for Autonomous Crane Handling
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Solution Overview
Problem
The challenge of automating crane operations on container vessels is hindered by the need for manual interaction, varying container types, and the movement of vessels during berthing, which requires real-time updates and accurate cargo information for efficient and autonomous operation.
Innovation Solution
A method using machine learning algorithms to process image data from cameras mounted on cranes, combined with LiDAR data, to determine the position of containers on a container bay, enabling precise detection and positioning through object detection and instance segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual interaction is used for crane operations, then operational flexibility is maintained, but automation level and productivity are reduced
Solution Approach 1:
The system enables self-service automation where the crane automatically detects container positions using cameras and machine learning algorithms, processes information independently, and executes operations without continuous manual intervention, thereby increasing automation level while maintaining operational flexibility through autonomous decision-making capabilities
2Measurement precision
If detailed real-time cargo information is obtained, then positioning precision is improved, but information processing complexity and time are increased
Solution Approach 1:
The patent replaces complex manual information gathering and processing systems with automated optical detection systems (cameras) and machine learning algorithms that automatically extract container position information from images, significantly reducing processing complexity while maintaining high positioning precision through computational methods
3Reliability
If multiple cameras and sensors are deployed, then detection accuracy is improved, but device complexity and cost are increased
Solution Approach 1:
The system employs multi-functional integration where cameras serve both positioning and object detection functions, and the machine learning algorithm processes information from multiple sources (images, LiDAR data, GPS) through a unified processing architecture, thereby improving detection accuracy while managing system complexity through functional consolidation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables fully autonomous crane operations, enhancing efficiency and speed by providing detailed container information, including size, type, and presence of other objects, thereby optimizing terminal operations.
Implementation Method 1
receiving image data from at least one camera mounted on a structure of a crane
Implementation Method 2
the memory configured for storing the image data, LiDAR data, and/or position data being representative of a position of the camera mounted on the structure of the crane
Data Source
AI summary
A method for determining a position of a container on a container bay of a container vessel is described. The method comprises receiving image data from at least one camera mounted on a structure of a crane, wherein the structure of the crane at least partly extends over the container bay. The image data is representative of a first image and at least a second image each showing at least an area of the container bay in which the container is arranged. The method further comprises determining the position of the container depending on the image data by a machine learning algorithm.