Crane-Mounted Sensor Fusion for Container Bay Position Detection
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Solution Overview
Problem
The challenge of automating crane operations on container vessels is hindered by the large variety of container types and the need for real-time updates due to vessel movement, which requires detailed and reliable cargo information that is often insufficient for autonomous operation.
Innovation Solution
A method utilizing combined image and lidar data from cameras and lidar devices mounted on a crane structure to determine properties of containers, such as position, size, and type, enabling accurate and autonomous crane operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If detailed cargo information is obtained through manual interaction and observation, then the reliability of cargo information improves, but the time required for information gathering increases and productivity decreases
Solution Approach 1:
The patent replaces manual observation and interaction with automated optical sensing systems (cameras) and laser ranging systems (lidar). These systems automatically capture container position, size, and type information without requiring crane operators to manually inspect containers, thereby maintaining high information reliability while enabling faster automated operations.
Solution Approach 2:
The determination device autonomously determines container properties by processing image data and lidar data itself, without requiring manual verification or intervention. The system self-services by automatically comparing obtained data with cargo information and identifying discrepancies, enabling rapid automated decision-making for crane operations.
2Productivity
If autonomous operation is implemented without sufficient cargo information, then productivity increases, but the reliability and accuracy of container property determination deteriorates
Solution Approach 1:
The patent merges data from multiple sensing systems (camera for visual information and lidar for dimensional data) to comprehensively determine container properties. By combining these independent measurement sources, the system achieves high measurement precision for container position, size, and type, enabling accurate autonomous operation without sacrificing productivity.
Solution Approach 2:
The system continuously compares obtained container property data with cargo information from the vessel's management system, identifying discrepancies and using this feedback to refine determinations. This closed-loop feedback mechanism ensures high accuracy in container property determination while maintaining rapid automated operation speeds.
3Measurement precision
If multiple sensing systems are integrated to improve measurement precision, then device complexity increases, but the ease of operation and automation capability improves
Solution Approach 1:
The determination device is designed as a multi-functional integrated system that simultaneously processes image data from cameras and ranging data from lidar devices to determine multiple container properties (position, size, type) in one operation. This universal approach consolidates multiple measurement functions into a single device, managing complexity while maintaining high measurement precision across all container attributes.
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 high-speed and efficient crane operations by providing precise information about containers, facilitating autonomous operation and enhancing the overall efficiency of container terminals.
Implementation Method 1
receiving first image data from a camera mounted on a structure of a crane, wherein the first image data are representative of a first image showing at least an area of the container bay
Implementation Method 2
receiving first lidar data from a lidar device mounted on a structure of the crane, wherein the first lidar data are representative of a first point cloud representing at least a part of the area of the container bay
Data Source
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AI summary
A method for determining at least one property of a container (24) on a container bay (22) of a container vessel (20) is described. The method comprises: receiving first image data from a camera (40) mounted on a structure of a crane (30), wherein the structure of the crane (30) at least partly extends over the container bay (22) and wherein the first image data are representative of a first image showing at least an area of the container bay (22) in which the container (24) is arranged; and receiving first lidar data from a lidar device (50) mounted on a structure of the crane (30), wherein the first lidar data are representative of a first point cloud representing at least a part of the area of the container bay (22); combining the received first image data and the received first lidar data; and determining the property of the container (24) depending on the combined data.