Omnidirectional Surface Vehicle for AI Net Anomaly Inspection
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Solution Overview
Problem
The risk of fish escaping from aquaculture facilities due to cage net failures poses significant economic and environmental challenges, and traditional human inspections are hazardous and inefficient.
Innovation Solution
An omnidirectional surface vehicle (OSV) equipped with buoyant compartments, thrusters, and a camera system for navigating and inspecting underwater structures, utilizing AI and ML to detect anomalies such as holes in aquaculture nets, enabling remote and automated inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human divers are used to inspect cage nets, then inspection can be performed visually and flexibly, but health and safety risks to divers and potential loss of fish stock occur
Solution Approach 1:
The patent introduces an omnidirectional surface vehicle (OSV) as an intermediary device between human inspectors and the underwater cage nets. The OSV is equipped with cameras and imaging systems that capture images of the nets from the surface, eliminating the need for divers to physically enter the water. This mediator approach maintains inspection effectiveness while removing direct human exposure to water-related hazards and fish escape risks.
Solution Approach 2:
The patent replaces the mechanical approach of human divers physically swimming and manually inspecting nets with an automated optical system. The OSV uses cameras, image processors, and computer vision algorithms to detect net integrity automatically. This substitution eliminates the need for human physical presence in water while maintaining or improving inspection capability through automated image analysis.
2Productivity
If traditional manual inspection methods are used, then equipment complexity is low, but inspection speed and efficiency are limited
Solution Approach 1:
The OSV is designed as a multi-functional platform that combines navigation capabilities (omnidirectional movement, positioning systems), imaging functions (cameras, lighting), and automated analysis (image processing, anomaly detection). This universal device performs multiple tasks - navigation, image capture, image processing, and defect identification - in a single integrated system, achieving high inspection speed without requiring multiple separate equipment systems.
Solution Approach 2:
The inspection system incorporates automated image processing and anomaly detection algorithms that enable the OSV to independently analyze captured images and identify net defects without continuous human intervention. The system self-evaluates image quality, automatically adjusts inspection parameters, and generates inspection reports, reducing the need for manual post-processing and increasing overall inspection efficiency.
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 OSV provides a safe, efficient, and cost-effective method for detecting and locating net damage, reducing human risk and increasing inspection speed and accuracy in aquaculture facilities.
Implementation Method 1
two or more buoyant compartments configured to support the OSV on a surface of a body of water
Implementation Method 2
a thruster configured to positionally navigate the OSV, wherein operation of the thruster causes fluid to be drawn into a ducted channel or ejected from the ducted channel
Data Source
AI summary
An omnidirectional surface vehicle (OSV) for use in a water-borne environment is described. The OSV can comprise a platform of interconnected buoyant compartments having incorporated position thrusters to navigate the OSV. The thrusters are connected to a series of ducts and ports to enable navigation of the OSV by fluid intake/ejection. An onboard camera system can be configured to capture imagery of a subsurface structure such as a net in an aquaculture facility. AI and ML technologies can be applied to enable detection of a potential anomaly/hole in the net structure. Location of the anomaly can be determined based on any of a current position/location of the OSV, a current field of view of the camera, a position/focal length of a lens in the camera, etc. Control/operation of the OSV can be performed autonomously by an onboard computer/controller. The OSV can be further configured to communicate with a remote device.