USV Multi-Camera Proximity Recognition for Collision Risk Awareness
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Solution Overview
Problem
Unmanned ships face challenges in navigating through unpredictable sea conditions and maintaining collision avoidance due to reduced visibility and the difficulty in adjusting speed or direction, especially in adverse weather, making them prone to unexpected collisions with other ships and obstacles.
Innovation Solution
A system utilizing multiple cameras and navigation sensors on an unmanned surface vehicle (USV) to detect obstacles, track objects, and estimate collision risks, supported by a remote navigation control device that provides situational awareness through augmented or virtual reality displays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple cameras and navigation sensors are used to detect obstacles and provide situational awareness, then collision avoidance capability is improved, but device complexity increases
Solution Approach 1:
The system divides the monitoring task into multiple specialized sensors (thermal imaging camera, panoramic camera, 360-degree camera, GPS, gyro sensor, AIS, RADAR), each responsible for specific detection functions. This segmentation allows the system to achieve comprehensive situational awareness through coordinated operation of specialized components rather than relying on a single complex sensor system.
Solution Approach 2:
The remote navigation control device integrates multiple sensor inputs and processing functions into a single unified system that performs obstacle detection, tracking, collision risk estimation, and situational awareness provision. This multi-functional integration reduces overall system complexity by consolidating diverse functions into one coordinated platform.
2Reliability
If real-time monitoring and collision risk estimation are performed using multiple images and navigation information, then navigation safety is improved, but information processing time and computational load increase
Solution Approach 1:
The system performs preliminary processing of images and navigation information as they are acquired, continuously updating object detection and tracking data before collision risk estimation is required. This preliminary action ensures that when collision risk calculation is needed, the data is already prepared and processed, reducing the time required for final safety assessments.
Solution Approach 2:
The system continuously monitors the environment, detects objects, tracks their movement, and updates collision risk estimates in real-time based on changing conditions. This feedback loop allows the system to adapt to dynamic situations efficiently, processing only the necessary changes rather than reprocessing all data from scratch, thereby reducing computational load and processing time.
Data Source
AI summary
A system and method for multi-image-based vessel proximity situation recognition support is proposed. The system may include an unmanned surface vehicle (USV) configured to detect and track surrounding objects by monitoring surroundings using surrounding images and navigation sensors. The system may also include a remote navigation controller configured to support proximity situation recognition of the unmanned surface vehicle according to detection of the surrounding objects, wherein the unmanned surface vehicle may include an image acquisition processor, a navigation sensor, and a detector.