Stochastic Route Determination via Neural Network Prediction
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Solution Overview
Problem
Current methods for determining optimized routes in dynamic and uncertain environments, such as ocean currents and waves, are inefficient and lack computational speed, particularly in applications like shipping and vehicle navigation, where time and risk optimization are critical.
Innovation Solution
A method using dynamically orthogonal reduced-order projections and stochastic path-planning techniques, combined with decision theory, to efficiently solve partial differential equations and determine optimized routes by predicting a distribution of stochastic paths, resulting in a significant computational speed-up compared to Monte Carlo techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Monte Carlo techniques are used for stochastic path-planning, then route optimization accuracy is maintained, but computational speed deteriorates significantly
Solution Approach 1:
The patent replaces the conventional Monte Carlo simulation approach (mechanical computational system) with a machine learning-based predictive model. The system trains a neural network to predict optimal routes directly, substituting the iterative sampling process with a direct prediction function, achieving both speedup and maintained accuracy
Solution Approach 2:
The system performs preliminary training of the machine learning model using historical flow data and Monte Carlo simulations before actual route planning. This pre-computation phase stores learned patterns that enable rapid prediction during operation, avoiding repeated expensive simulations
2Measurement precision
If detailed dynamic flow information is processed to improve route accuracy, then navigation precision is improved, but computational complexity increases
Solution Approach 1:
The system transforms the complex partial differential equations describing fluid flow into a parameterized form suitable for neural network input. By changing the mathematical representation from continuous PDEs to discrete parameter sequences, the system maintains accuracy while enabling efficient computational processing through the ML model
Data Source
AI summary
Techniques for use in connection with determining an optimized route for a vehicle include obtaining a target state, a fixed initial position of the vehicle, and dynamic flow information, and determining an optimized route from the fixed initial position to the target state using the dynamic flow information.