Drone Fault Detection via Context-Adaptive Test Execution
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Solution Overview
Problem
Testing and debugging drones pose challenges due to factors like geo-location, altitude, and environmental conditions, which can pose risks to the drone and existing methods are inadequate for effective fault detection and remediation during flight.
Innovation Solution
A computer-implemented method and system that dynamically determine and apply test cases to drones based on context parameter data, such as weather and drone conditions, to identify and remediate faults while ensuring safety and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional testing methods are used on drones during flight, then fault detection capability is improved, but safety risks increase due to geo-location, altitude, and environmental factors
Solution Approach 1:
The system dynamically selects and executes test cases based on real-time context parameters such as geo-location, altitude, and environmental conditions. The test plan is not fixed but adapts continuously during flight operations, allowing the drone to perform appropriate testing while maintaining safety margins based on current operational context.
Solution Approach 2:
The system changes testing parameters dynamically by adjusting which test cases are executed based on context parameter data. Different test suites are selected depending on flight conditions, allowing comprehensive fault detection in safe conditions while reducing or skipping tests when safety risks are elevated.
2Measurement precision
If comprehensive test cases are executed during flight, then fault detection accuracy is improved, but system complexity increases
Solution Approach 1:
The test suite is segmented into multiple discrete test cases that can be independently selected and executed. Rather than running a monolithic comprehensive test suite, the system divides testing into modular units that can be selectively applied based on context, reducing overall system complexity while maintaining detection accuracy when needed.
Solution Approach 2:
The system executes only the necessary portion of test cases based on current flight context and detected faults. Instead of always running complete test suites, it applies partial testing appropriate to the situation, reducing complexity while maintaining sufficient detection accuracy for the given operational context.
3Productivity
If real-time context monitoring is implemented for test selection, then testing effectiveness is improved, but data processing requirements increase
Solution Approach 1:
Context parameters and test case selection criteria are pre-established and configured before flight operations begin. The system pre-defines which test cases should be executed under specific contextual conditions, allowing rapid decision-making during flight without requiring complex real-time analysis, thus reducing data processing requirements while maintaining testing effectiveness.
Data Source
AI summary
A computer-implemented method includes obtaining fault information regarding a fault associated with a first drone. The computer-implemented method additionally includes obtaining context parameter data of the first drone. The computer-implemented method additionally includes, responsive to obtaining the fault information and the context parameter data, determining to apply a first test case of a plurality of test cases based on a first risk value determined for the first test case using the context parameter data. The first test case is associated with the fault. The computer-implemented method additionally includes causing the first drone to initiate execution of the first test case.