Marine Proximity Sensing Using 2D Vessel Outline Prioritization
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Solution Overview
Problem
Current proximity sensing systems for autonomous and semi-autonomous marine vessels generate large amounts of data, which is impractical for embedded controllers and bandwidth-limited networks, and existing solutions are insufficient for filtering and prioritizing data effectively within existing marine vessel control architectures.
Innovation Solution
A simplified two-dimensional vessel outline is used to calculate proximity measurements and prioritize data, reducing computational loads by identifying the most important object dataset, which includes linearly and rotationally closest proximity measurements, and translating data into a common reference frame for navigation control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive proximity measurements from multiple sensors are collected for autonomous navigation, then navigation safety and collision avoidance are improved, but data processing complexity and computational load increase significantly
Solution Approach 1:
The patent segments the continuous proximity data stream into discrete angular zones (e.g., 0-45 degrees, 45-90 degrees) relative to the vessel's centerline. Each zone is processed independently to identify closest objects, reducing the computational complexity from analyzing all sensor data globally to processing smaller angular segments separately, then combining results.
Solution Approach 2:
The patent extracts only the most critical proximity measurements - specifically the closest objects in each angular zone - and discards redundant data. This extraction principle focuses computational resources on the most important objects that pose the greatest collision risk, rather than processing all proximity measurements equally.
2Measurement precision
If detailed three-dimensional vessel models are used for accurate proximity calculation, then measurement precision is improved, but computational load and processing time increase
Solution Approach 1:
The patent applies local quality by using simplified two-dimensional top-down vessel outlines for most proximity calculations, reserving three-dimensional modeling only for critical scenarios where vertical clearance is relevant. This approach maintains adequate measurement precision for horizontal proximity while significantly reducing computational load compared to using full 3D models for all calculations.
Solution Approach 2:
The patent changes the dimensional parameter of the vessel model from three-dimensional to two-dimensional for top-down proximity calculations. This parameter change reduces computational complexity while maintaining sufficient accuracy for detecting objects at the vessel's horizontal boundaries, where collision risk is highest.
3Measurement precision
If all proximity measurement data are transmitted to the navigation controller, then navigation control accuracy is improved, but network bandwidth requirements and communication load increase
Solution Approach 1:
The processor extracts and transmits only the most important proximity data - specifically the closest objects in each angular zone and their relevant parameters - to the navigation controller. This selective extraction reduces the volume of transmitted data while maintaining the precision needed for safe navigation decisions.
Solution Approach 2:
The patent segments proximity data into angular zones and transmits only the critical measurements from each zone (closest objects) rather than all sensor data. This segmentation approach reduces overall data transmission volume while ensuring that navigation control receives the most relevant information for each directional sector.
4Productivity
If simplified vessel outlines are used for proximity calculation, then computational load is reduced, but measurement precision may be compromised
Solution Approach 1:
The patent changes the model dimension from 3D to 2D for top-down proximity calculations, achieving significant computational efficiency gains. The simplified two-dimensional vessel outlines accurately represent the vessel's horizontal footprint, maintaining sufficient precision for detecting objects at the vessel's boundaries where collision risk is highest.
Solution Approach 2:
The patent applies local quality by using simplified 2D outlines for horizontal proximity detection and reserving 3D models for vertical clearance calculations when needed. This approach maintains adequate measurement precision for the most critical collision scenarios while achieving the computational efficiency required for real-time processing.
Data Source
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AI summary
A proximity sensor system on a marine vessel (10) includes one or more proximity sensors (72, 74, 76, 78), each at a sensor location on the marine vessel (10) and configured to measure proximity of objects and generate proximity measurements (90). A processor (70) is configured to receive the proximity measurements (90) measured by one or more proximity sensors (72, 74, 76, 78) on the marine vessel (10), and identify four linearly-closest proximity measurements (90+x, 90-x, 90+y, 90-y) based on a stored vessel outline (80), including one closest proximity measurement in each of a positive X direction, a negative X direction, a positive Y direction, and a negative Y direction. The processor (70) then generates a most important object (MIO) dataset identifying the four linearly-closest proximity measurements (90+x, 90-x, 90+y, 90-y).