Integrating Vision Systems and STPA for Robust Landing and Take-Off in VTOL Aircraft

TL;DR

This paper combines vision-based sensor fusion with System-Theoretic Process Analysis (STPA) to improve safety and robustness during VTOL UAV take-off and landing in uncertain environments.

Contribution

It introduces an integrated approach that incorporates fiducial markers and hazard analysis to identify unsafe controls and develop mitigation strategies for VTOL UAVs.

Findings

Enhanced safety through hazard identification and mitigation strategies.

Improved reliability of vision-based control during critical flight phases.

Framework applicable to resilient autonomous VTOL systems.

Abstract

Vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) are versatile platforms widely used in applications such as surveillance, search and rescue, and urban air mobility. Despite their potential, the critical phases of take-off and landing in uncertain and dynamic environments pose significant safety challenges due to environmental uncertainties, sensor noise, and system-level interactions. This paper presents an integrated approach combining vision-based sensor fusion with System-Theoretic Process Analysis (STPA) to enhance the safety and robustness of VTOL UAV operations during take-off and landing. By incorporating fiducial markers, such as AprilTags, into the control architecture, and performing comprehensive hazard analysis, we identify unsafe control actions and propose mitigation strategies. Key contributions include developing the control structure with vision system capable of identifying a fiducial marker, multirotor controller and corresponding unsafe control actions and mitigation strategies. The proposed solution is expected to improve the reliability and safety of VTOL UAV operations, paving the way for resilient autonomous systems.