Automated System-level Testing of Unmanned Aerial Systems
The paper presents AITester, an AI-driven approach to automating system-level testing for unmanned aerial systems (UAS). Traditional methods rely on manually creating, executing, and evaluating test scenarios, which are time-consuming and error-prone. AITester leverages model-based testing and AI to dynamically generate, execute, and assess scenarios based on real-time environmental conditions. The approach has been successfully tested on key UAS components, detecting deviations in the UAV autopilot and identifying potential issues in the ground control station’s cockpit display systems, proving its effectiveness in enhancing UAS software testing
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Automated System-level Testing of Unmanned Aerial Systems
An overview of the approach showing the proposed UAS profile, inputs required from avionics testers, AITester, and UAV Environment. The dotted arrows ( ) represent the compliance of models to the UAS profile and solid arrows ( ) represent the information/control flow.
