Multi-Sensor Ship Navigation for Dynamic Collision Avoidance
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Solution Overview
Problem
Conventional navigation assistance devices on ships lack integration of multiple sensors to provide dynamic and flexible collision avoidance routes, often relying solely on individual navigator judgment, and may not function without essential sensors like Radar, AIS, and GPS, failing to account for varying ship configurations and environmental conditions.
Innovation Solution
A method and system using a plurality of sensors, including image and distance detection sensors, to identify objects, calculate collision risk, and determine avoidance paths, integrating data from Radar, Lidar, AIS, and other sensors to provide real-time navigation assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors (Radar, AIS, GPS, Lidar, camera) are integrated to improve collision avoidance capability, then navigation safety and flexibility are improved, but device complexity increases
Solution Approach 1:
The system segments sensor integration by creating a modular sensor management module that handles different sensor types (Radar, AIS, GPS, Lidar, camera) independently. Each sensor data stream is processed separately through standardized interfaces, allowing the system to scale from simple to complex configurations without redesigning the entire system architecture.
Solution Approach 2:
The navigation assistance device implements a universal sensor interface framework that can accommodate multiple sensor types through common data processing pipelines. The system uses a unified coordinate system and data fusion algorithm that works across different sensor modalities, enabling the same hardware platform to support various sensor configurations.
2Measurement precision
If essential sensors (Radar, AIS, GPS) are required for full functionality, then measurement precision and reliability are improved, but ease of operation deteriorates due to installation requirements
Solution Approach 1:
The system implements partial functionality based on available sensors. If full sensor suites are not installed, the device automatically adjusts its operational capabilities to work with the subset of available sensors. For example, if Radar is unavailable, the system relies more heavily on AIS and camera data, maintaining acceptable detection accuracy without requiring complete sensor installations.
Solution Approach 2:
The system dynamically changes operational parameters based on sensor availability. Detection thresholds, processing frequencies, and data fusion weights are adjusted according to which sensors are present. This allows the same hardware to achieve acceptable measurement precision across different installation configurations without manual reconfiguration.
3Measurement precision
If comprehensive sensor data processing is performed to improve object identification accuracy, then measurement precision is improved, but loss of time increases due to processing requirements
Solution Approach 1:
The system performs preliminary processing of sensor data, particularly camera images, by pre-calculating feature descriptors and object candidates before full analysis is required. Bounding boxes and initial object detections are generated in advance, reducing the computational burden during critical real-time decision-making periods.
Solution Approach 2:
The system maintains continuous processing of sensor data streams with overlapping computation windows. Instead of batch processing images, the system continuously analyzes incoming video frames with sliding windows, ensuring that object detection and tracking are always underway without interruption, thereby reducing effective processing time per object while maintaining accuracy.
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 identification of objects, predicts collision risk, and derives dynamic avoidance paths, even with incomplete sensor setups, reducing image preprocessing time and distinguishing between dynamic and static objects.
Implementation Method 1
obtaining an image frame in real time using a first sensor
Implementation Method 2
obtaining data included in the object area using a second sensor
Implementation Method 3
calculates a risk of collision between a host ship and the object by fusing the image frame information of the object and the information about the object
Data Source
AI summary
A navigation assistance system using a plurality of sensors according to an aspect of the present disclosure includes a display configured to provide a monitoring image, a sensor unit including at least one sensor, a memory in which at least one program is stored, and at least one processor configured to execute the at least one program, wherein the at least one processor obtains image frame information of an object using a first sensor, calculates information about the object using a second sensor, calculates a risk of collision between a host ship and the object by fusing the image frame information of the object and the information about the object, and determines an avoidance path based on the calculated collision risk.