Berthing Line Generation from Clustered Ship Measurement Points
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Solution Overview
Problem
Existing berthing support systems for ships struggle to accurately calculate parameters like distance, speed, and angle with respect to the berthing place, particularly in generating a straight line along the side surface of the berthing place.
Innovation Solution
An information processing device that acquires measurement data from a ship-mounted measurement device, generates clusters by dividing measured points, and then generates a straight line along the berthing place based on these clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional measurement methods are used to calculate berthing parameters, then the system can operate with simple processing, but the measurement precision and accuracy of distance, speed, and angle calculations deteriorate
Solution Approach 1:
The patent applies segmentation by dividing the measured point cloud data into multiple clusters based on spatial distribution and characteristics. Each cluster represents a specific region or feature of the berthing place, allowing the system to process complex data in manageable segments. This clustering approach enables accurate identification of the side surface of the berthing place while maintaining computational efficiency through localized processing of divided data groups.
2Manufacturing precision
If simple processing methods are used, then the device complexity is low, but the ability to accurately generate a straight line along the side surface of the berthing place deteriorates
Solution Approach 1:
The patent segments the point cloud data into clusters that represent different regions of the berthing place. By dividing the data spatially, the system can identify and process the side surface region separately, enabling accurate straight line generation through localized analysis of relevant clusters rather than processing all data points uniformly.
Solution Approach 2:
The patent applies local quality by selecting and processing only the clusters that correspond to the side surface of the berthing place, rather than uniformly processing all measured points. This localized processing approach concentrates computational resources on the relevant regions, improving the accuracy of straight line generation while reducing unnecessary processing of unrelated data areas.
3Loss of information
If comprehensive measurement data is collected from all areas, then the data completeness is high, but the difficulty of detecting and measuring the relevant features increases
Solution Approach 1:
The patent segments the comprehensive measurement data into distinct clusters based on spatial distribution and characteristic features. This segmentation preserves all collected information while organizing it into manageable groups, making it easier to identify and analyze the side surface features by examining specific clusters rather than searching through the entire dataset.
Solution Approach 2:
The patent enhances local quality by assigning different processing priorities and analysis methods to different clusters based on their characteristics. Clusters identified as representing the side surface receive focused attention and specialized processing, while other clusters are handled differently, thereby reducing the overall difficulty of detecting relevant features while maintaining complete data utilization.
Data Source
AI summary
The information processing device includes an acquisition means, a cluster generation means and a straight line generation means. The acquisition means acquires measurement data which is a set of data representing a plurality of measured points of a berthing place measured by a measurement device provided on a ship. The cluster generation means generates one or more clusters obtained by dividing the plurality of measured points by clustering. The straight line generation means configured to generate a straight line along the berthing place of the ship, based on the one or more clusters.