UAV Simulation Test Case Generation for High-Fidelity Autonomy Training
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Solution Overview
Problem
Current testing methods for autonomous flight systems are inadequate in simulating real-world environments and conditions, particularly for complex and dangerous scenarios, leading to inefficiencies and risks in training and testing.
Innovation Solution
A system and method for generating and training test cases using simulated events, which involve monitoring outputs, detecting target conditions, modifying simulation parameters, and generating new test cases to simulate various scenarios, including realistic turbulence and flight paths, to provide high-fidelity simulations for flight autonomy systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional testing methods are used for autonomous flight systems, then the testing process is simpler and requires fewer resources, but the simulation fidelity is insufficient and cannot accurately represent real-world conditions
Solution Approach 1:
The patent creates virtual copies of real-world flight environments, aircraft, and operational conditions through high-fidelity simulation. The system generates synthetic test data that replicates actual flight scenarios, allowing autonomous systems to be trained and tested in realistic virtual environments without requiring physical aircraft for every test case
Solution Approach 2:
The system performs preliminary generation of diverse test cases and simulation scenarios before actual testing begins. By pre-generating edge cases, failure scenarios, and environmental conditions in the virtual environment, the system prepares comprehensive test datasets that would be difficult or dangerous to obtain through traditional physical testing
2Reliability
If high-fidelity simulation with diverse scenarios is implemented, then the training effectiveness and decision-making accuracy improve, but the computational resources and testing time increase
Solution Approach 1:
The system generates a large excess of test cases and simulation scenarios, focusing computational resources on the most critical and diverse situations. By creating more test cases than strictly necessary, particularly for edge cases and failure modes, the system ensures comprehensive coverage of the operational envelope without requiring exhaustive testing of every possible scenario
Solution Approach 2:
The simulation system automatically generates its own test cases and validates results without requiring manual intervention for each test scenario. The autonomous flight system undergoes self-testing and validation through the simulated environments, reducing the need for human operators to manually design and execute each test case
3Stability of the object's composition
If realistic turbulence and dangerous scenarios are simulated, then the robustness of autonomous systems is improved, but the risk of harm to physical aircraft and infrastructure increases
Solution Approach 1:
The patent converts the potential harm of testing dangerous scenarios into a benefit by performing all stress testing in a virtual environment. Extreme conditions, failure modes, and edge cases that would be dangerous to test with physical aircraft are safely simulated, allowing the autonomous system to be robustly tested without exposing actual aircraft or infrastructure to harm
Solution Approach 2:
The high-fidelity simulation environment acts as an intermediary between the autonomous flight system and the physical world. This virtual intermediary allows dangerous and extreme test scenarios to be executed without direct interaction with physical aircraft, protecting real infrastructure while still providing realistic feedback to the autonomous system
Data Source
AI summary
Systems and methods for generating testing and training cases for a simulator based on simulated events are disclosed. The system can monitor an input from a first simulation of a first test case, and detect, based on the input from the first simulation, a target condition resulting from the first test case. The system can identify, based on the target condition, first simulation parameters of the first test case associated with the target condition. The system can generate a second test case having second simulation parameters by modifying the first simulation parameters of the first test case, and output the second test case to a flight autonomy system. The system can provide the generated test cases for the flight autonomy system and can monitor the real-time performance of the simulation of the flight autonomy system.