Aircraft Dynamics Modeling for Adversarial Trajectory Prediction
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Solution Overview
Problem
Traditional physics-based methods for predicting aircraft trajectories require a pre-existing model of the exact aircraft and may lack necessary state variables, making them unsuitable for autonomous systems, while existing learning-based methods fail to accurately model aircraft dynamics.
Innovation Solution
A neural network-based method using a compressive encoding scheme with residual skip connections and loss functions to predict aircraft trajectories, handling angular variables and missing parameters, and extending model-based reinforcement learning to adversarial multi-agent environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physics-based dynamic equations are used to predict aircraft trajectories, then prediction accuracy is improved, but the method requires a pre-existing model of the exact aircraft and specific state variables that may not be available to autonomous systems
Solution Approach 1:
The patent replaces physics-based mechanical models with a neural network-based learning system. The neural network learns aircraft dynamics from observed state transitions without requiring explicit physics equations or pre-existing aircraft models, enabling autonomous systems to predict trajectories using only available sensor data.
Solution Approach 2:
The patent creates a learned copy of the aircraft dynamics through a neural network that mimics the behavior of the actual aircraft. This learned model replicates trajectory predictions without needing the original physics-based model or unavailable state variables, allowing autonomous agents to operate with limited information.
2Adaptability or versatility
If traditional learning-based methods are used to model aircraft dynamics, then adaptability to autonomous systems is improved, but prediction accuracy deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the neural network's predictions are continuously refined by comparing learned observations with actual observed states. The loss function computes the difference between predicted and actual next states, and gradient descent updates the network weights to minimize this error, progressively improving prediction accuracy while maintaining adaptability.
Solution Approach 2:
The patent performs preliminary learning of aircraft dynamics during a training phase before autonomous operation. The neural network is trained on historical state transition data to learn the underlying dynamics model in advance, so that during autonomous operation, the system can make accurate predictions without requiring real-time physics calculations or unavailable state variables.
Data Source
AI summary
Training adversarial aircraft controllers is provided. The method comprises inputting current observed states of a number of aircraft into a world model encoder, wherein each current state represents a state of a different aircraft, and wherein each current state comprises a missing parameter value. A number of adversarial control actions for the aircraft are input into the world model encoder concurrently with the current observed state, wherein the adversarial control actions are generated by competing neural network controllers. The world model encoder generates a learned observation from the current observed states and adversarial control actions, wherein the learned observation represents the missing parameter value from the current observed states. The learned observation and current observed states are input into the competing neural network controllers, wherein each current observed state is fed into a respective controller. The competing neural network controllers then generate next adversarial control actions.