Pursuer Neural Network Training With Neural ODE USV Tracking
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Solution Overview
Problem
Existing pursuit-evasion strategies for Unmanned Surface Vehicles (USVs) face challenges in real-world applications due to the inadequacies of classical control and machine learning methods, such as MPC and Reinforcement Learning (RL), which require simplifications and are inefficient in handling complex environments and uncertainties.
Innovation Solution
A computer-implemented method involving a pursuer neural network agent trained using an ordinary differential equation solver, where dynamic parameters are updated iteratively through a simulation with an evader neural network agent, optimizing weights based on a loss function that incorporates desired tracking range, collision avoidance, and vehicle dynamics constraints, enhancing the pursuer's ability to navigate and intercept evaders effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional control techniques like Model Predictive Control (MPC) are used for pursuit-evasion games, then analytical solutions can be derived, but numerous simplifications are required resulting in diminished performance in real-world applications
Solution Approach 1:
The patent replaces conventional control techniques (MPC) and traditional machine learning (RL) with a neural ordinary differential equation (ODE) solver that incorporates physics-based vehicle dynamics models. This substitution allows the system to solve pursuit-evasion problems without the simplifications required by MPC or the training inefficiencies of RL, achieving both ease of implementation and high real-world performance.
Solution Approach 2:
The patent changes the fundamental parameters of the approach by using neural ODE solvers that can handle continuous-time dynamics directly, rather than discrete-time approximations. This allows the system to work with the full complexity of vehicle dynamics without requiring simplifying assumptions, while maintaining computational efficiency through the differentiability of the ODE solver.
2Loss of information
If game theory methodology is applied to pursuit-evasion problems, then analytical solutions can be obtained, but deriving these solutions requires extensive calculus and many assumptions leading to reduced effectiveness in real-world applications
Solution Approach 1:
The patent substitutes game theory's analytical approaches with a neural ODE-based simulation framework. This replacement eliminates the need for extensive calculus derivations and assumptions about threat potential fields, while maintaining complete information about vehicle dynamics and pursuit-evasion interactions through physics-based modeling.
3Speed
If Reinforcement Learning (RL) methods are used for pursuit-evasion problems, then real-time inference can be performed rapidly, but training efficiency is heavily dependent on reward function design and state space exploration becomes computationally complex
Solution Approach 1:
The patent replaces Reinforcement Learning with a neural ODE solver that uses physics-based vehicle dynamics models. This substitution eliminates the need for extensive training with carefully engineered reward functions, while maintaining the ability to perform rapid real-time inference through efficient ODE solving. The system achieves both fast inference and reduced training requirements by leveraging differentiable physics simulations.
4Device complexity
If classical control and optimization theory are applied to USV pursuit-evasion, then structured approaches can be implemented, but the methods require numerous simplifications resulting in diminished performance
Solution Approach 1:
The patent changes the methodology by using neural ODE solvers that can handle continuous-time dynamics with full vehicle dynamics models. This approach maintains the structured nature of control theory while eliminating the need for simplifying assumptions, allowing the system to account for complex USV behaviors, uncertainties, and environmental factors without losing performance.
Data Source
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AI summary
The present invention relates to a computer-implemented method of training a pursuer neural network agent. The method comprising: providing an evader neural network agent trained to update iteratively a plurality of dynamic parameters of an evader, a pursuer neural network agent initialised to update iteratively a plurality of approximators for a pursuer, and an ordinary differential equation solver configured to update a plurality of dynamic parameters of the pursuer based on the updated approximators; sampling initial dynamic parameters from a set of initial dynamic parameters of the evader; running a simulation involving the pursuer neural network agent pursuing the evader neural network agent based on the sampled initial dynamic parameters; computing a loss based on the dynamic parameters of the pursuer from the ordinary differential equation solver and the dynamic parameters of the evader from the evader neural network agent; and optimising weights of the pursuer neural network agent based on the computed loss.