Vessel Situation Awareness Mapping with Selective Sensor Fusion
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Solution Overview
Problem
Current navigation systems for unmanned vessels face challenges due to sensor limitations, weather interference, and occlusions, which impair the accuracy of self-navigation maps, and existing data sharing methods lack selectivity, leading to inefficient data communication and potential navigation hazards.
Innovation Solution
A system that utilizes a control hub to aggregate data from various sensors, request and share relevant real-time map data between vessels, and deploy drones to collect missing data, while using selective communication and advanced sensing systems like lidar, radar, and cameras, and leveraging geographic information systems and vessel traffic management systems to enhance map accuracy and predict dynamic obstacle movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are combined to compensate for limited range and accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple sensor types (lidar, radar, cameras, infrared imaging systems) into an integrated sensor sub-system that collects data from various sources. This merging approach compensates for the limited range and accuracy of individual sensors by fusing their outputs to create comprehensive map data with improved measurement precision.
Solution Approach 2:
The control hub serves multiple functions: it collects data from sensor sub-systems, processes map data, identifies gaps, requests additional data from other vessels or geographic information systems, and generates comprehensive navigation maps. This multi-functional design reduces overall system complexity by consolidating operations into a single coordinating unit.
2Loss of information
If selective data sharing is implemented between vessels, then loss of information is reduced, but device complexity increases
Solution Approach 1:
The control hub proactively identifies gaps in map data by comparing collected sensor data against expected environmental features. When gaps are detected, the system automatically requests relevant map data from other vessels or geographic information systems before navigation decisions are made, ensuring complete information is available in advance.
Solution Approach 2:
The control hub acts as an intermediary that selectively shares data between vessels based on relevance and need. Rather than implementing complex peer-to-peer data exchange protocols, the hub centralizes data collection and distribution, simplifying communication while ensuring each vessel receives only the specific map data it requires to fill identified gaps.
3Measurement precision
If drones are deployed to collect missing data, then measurement precision is improved, but productivity decreases
Solution Approach 1:
Instead of deploying drones for comprehensive area mapping, the system selectively sends drones only to specific gap areas where map data is missing or insufficient. This partial action approach focuses resources on critical information needs rather than exhaustive data collection, maintaining productivity while improving precision where necessary.
Solution Approach 2:
The control hub autonomously identifies map data gaps and determines when drone deployment is necessary without requiring manual intervention. The system self-manages the decision-making process for deploying unmanned aerial vehicles, analyzing sensor data quality and automatically initiating drone missions only when gap filling is required for navigation safety.
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
The system generates accurate and comprehensive real-time maps, reduces data traffic by providing only necessary information, and enables vessels to respond quickly to potential risks by predicting dynamic obstacle paths and providing alerts for defensive actions, thereby improving navigation accuracy and safety.
Implementation Method 1
sensor sub-systems located on a plurality of vessels and configured for sensing, in real-time, the presence of obstacles within a local environment of the vessel
Implementation Method 2
sensor sub-systems may comprise; a camera, an infrared imaging system, lidar, radar or any combination thereof
Data Source
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AI summary
A system for generating a map for use in the navigation of one or more vessels is described. The system comprises: one or more sensor sub-systems provided on a vessel and configured for sensing, in real-time, the presence of obstacles within a local environment of the vessel; a processor associated with the one or more sensor sub-systems for collecting the sensor data and configured for generating therefrom, in a serialised digital format, a real-time map of the local environment surrounding the vessel; data communication means between the processor and a remotely located hub, the hub in data communication with one or more remote sensing systems located remotely from the vessel. The hub is configured to collect additional sensor data from a source remote from the vessel and communicate the additional sensor data to the processor. The processor is configured on receipt of additional sensor data to generate a real-time map from the sensor data and the additional sensor data.