Marine Vessel Proximity Sensing Using 2D Outline MIO Prioritization
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Solution Overview
Problem
Existing proximity sensing systems for marine vessels generate large amounts of data impractical for embedded controllers and bandwidth-limited networks, with insufficient filtering and prioritization methods for autonomous or semi-autonomous navigation, especially in marine environments.
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 (MIO) dataset, which includes closest proximity measurements in six directions, utilizing low-processor-demand filtering and geometric calculations, and implementing these in real-time microcontrollers for existing navigation control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional proximity sensing systems process all raw sensor data, then measurement precision is improved, but device complexity and computational load increase significantly
Solution Approach 1:
The patent extracts only the most critical proximity measurements from the complete sensor dataset by identifying the minimum distance object in each of six directional zones (forward, backward, left, right, left-forward, right-backward). This selective extraction reduces data volume while preserving the most relevant navigation information, resolving the contradiction between measurement precision and computational complexity.
Solution Approach 2:
The patent segments the 360-degree surrounding environment into six distinct directional zones and processes proximity measurements independently within each zone. This segmentation allows the system to focus computational resources on identifying the closest object in each direction rather than processing all objects globally, thereby reducing overall computational load while maintaining comprehensive situational awareness.
2Loss of information
If comprehensive proximity data from all directions is transmitted, then information completeness is improved, but network bandwidth consumption increases
Solution Approach 1:
The system extracts only the essential proximity information (minimum distance and bearing to the closest object) from each of the six directional zones before transmission. This extraction approach ensures that the most critical navigation-relevant data is communicated while eliminating redundant measurements, thus maintaining information completeness for autonomous navigation while significantly reducing network bandwidth consumption.
3Manufacturing precision
If detailed processing of all proximity measurements is performed, then navigation control precision is improved, but processing time increases
Solution Approach 1:
The patent divides the complex task of proximity analysis into six independent directional zones, each processed separately to identify the closest object. This segmentation enables parallel processing of different directions and reduces the overall computational complexity from analyzing all objects globally to finding minimum distances in six zones, thereby maintaining navigation precision while reducing processing time for real-time control.
Solution Approach 2:
The system performs partial processing by focusing only on identifying the minimum distance object in each directional zone rather than analyzing all proximity measurements in detail. This partial action approach provides sufficient information for safe navigation control without the excessive processing time required for comprehensive analysis of all sensor data.
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 store a two-dimensional vessel outline (80) of the marine vessel (10) with respect to a point of navigation (Pn) for the marine vessel (10), 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) to the two-dimensional 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).