Submarine ML Navigation Control Under Communicative Isolation
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Solution Overview
Problem
The navigation of submarines and submersibles is complex due to the need to interpret various environmental parameters such as pressure, temperature, salinity, and currents, which can change unpredictably, making it challenging to control these watercraft effectively without external data sources.
Innovation Solution
The use of onboard machine learning algorithms to determine relationships between environmental parameters and corresponding actions of the watercraft, allowing for autonomous control and navigation towards targets without reliance on external data or communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional navigation techniques (dead reckoning, inertial navigation, bottom contour navigation) are used below periscope depth, then the submarine can navigate without external data sources, but the navigation becomes complex and less reliable due to unpredictable environmental parameter changes
Solution Approach 1:
The submarine uses its own operational data (speed, course, depth, pitch, roll, yaw) collected during actual voyages to train the machine learning algorithm. This self-service approach eliminates the need for external data sources while improving navigation reliability through adaptive learning from real operational experiences.
Solution Approach 2:
Traditional mechanical navigation systems (dead reckoning, inertial navigation) are replaced with a machine learning-based autonomous control system. The ML algorithm processes environmental parameters and submarine state data to automatically determine navigation actions, reducing control complexity while improving reliability through adaptive decision-making.
2Adaptability or versatility
If machine learning algorithms are used for autonomous control, then navigation adaptability to changing environmental conditions improves, but the system complexity and training data requirements increase
Solution Approach 1:
The machine learning algorithm serves multiple functions: it processes various environmental parameters (pressure, temperature, salinity, density, tide, current), integrates submarine state data (speed, course, depth, pitch, roll, yaw), and autonomously determines navigation actions. This multi-functionality achieves high navigation adaptability while managing system complexity through a unified ML framework.
3Measurement precision
If communication with external sources is maintained for navigation data, then navigation accuracy improves, but the submarine loses communicative isolation and operational autonomy
Solution Approach 1:
The submarine achieves high-position determination accuracy through self-service by using its own operational data collected during voyages to train the ML algorithm. This eliminates the need for external communication while maintaining operational autonomy, as the system learns from and adapts to its own operational experiences.
Data Source
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AI summary
A method of training a machine learning, ML, algorithm to control a watercraft is described. The watercraft is a submarine or a submersible submerged in water. The method is implemented, at least in part, by a computer, comprising a processor and a memory, aboard the watercraft. The method comprises: obtaining training data including respective sets of environmental parameters and corresponding actions of a set of communicatively isolated watercraft, including a first watercraft; and training the ML algorithm comprising determining relationships between the respective sets of environmental parameters and the corresponding actions of the watercraft of the set thereof. A method of controlling a watercraft by a trained ML algorithm is also described.