Wave Glider Path Planning Under Acoustic Range Constraints
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Solution Overview
Problem
The wave glider's low average velocity and limited underwater acoustic communication range hinder its ability to maintain continuous and reliable information interaction with underwater vehicles, making synchronous navigation and positioning challenging.
Innovation Solution
A method and system utilizing a deep learning neural network for path planning, which acquires historical and real-time navigation data to optimize the wave glider's path, ensuring it stays within the communication range of the underwater vehicle, by fitting historical data nonlinearly and constructing optimized path schemes using a shore-based monitoring center and on-line end data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the wave glider uses underwater acoustic communication to collaborate with underwater vehicles, then information interaction is enabled, but the limited communication range restricts the application
Solution Approach 1:
The system performs preliminary path planning using deep learning neural networks to predict and optimize the wave glider's trajectory in advance, ensuring it stays within the limited acoustic communication range of the underwater vehicle before actual navigation occurs
Solution Approach 2:
The system continuously monitors real-time positions of both the wave glider and underwater vehicle, feeds this data back to the path planning algorithm, and dynamically adjusts the trajectory to maintain optimal communication distance within the acoustic range limit
2Reliability
If the wave glider follows the underwater vehicle, then collaborative navigation is achieved, but the low velocity of the wave glider prevents synchronous following
Solution Approach 1:
The path planning system dynamically adjusts the wave glider's velocity profile and trajectory in real-time based on the underwater vehicle's position and movement, creating a adaptive following strategy that accounts for the wave glider's slower speed while maintaining collaborative navigation
Solution Approach 2:
The system changes the navigation parameters by using deep learning to predict optimal speed adjustments and position corrections, allowing the wave glider to compensate for its lower velocity through intelligent path optimization rather than direct speed matching
3Adaptability or versatility
If the wave glider operates beyond acoustic communication range, then greater operational flexibility is achieved, but information interaction becomes discontinuous
Solution Approach 1:
The deep learning neural network performs preliminary analysis of the underwater vehicle's trajectory and communication range constraints, pre-calculating optimal path segments that balance operational flexibility with continuous communication coverage before the wave glider begins navigation
Data Source
AI summary
The invention relates to a method and system for path planning of a wave glider, comprising acquiring historical navigation data of the glider and an underwater vehicle via a shore-based monitoring center; fitting historical navigation data nonlinearly by a deep learning neural network to obtain a trained network; acquiring real-time navigation data of the glider at an off-line end, real-time navigation data and predetermined shipping track data of the vehicle; obtaining the set of off-line optimized path planning schemes of the glider by the above data and the trained network; and determining an optimal path planning scheme of the glider by the deep learning neural network according to real-time data and constraint data of the glider at the on-line end. The invention can reasonably plan the path of the glider and ensure continuous and reliable information interaction between the glider and the vehicle.