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

TL;DR

The paper tackles safety challenges during VTOL UAV take-off and landing in uncertain environments by integrating vision-based sensor fusion with System-Theoretic Process Analysis (STPA). It develops a vision-enabled control structure using fiducial markers (AprilTags) and conducts a detailed STPA hazard analysis to identify unsafe control actions and mitigation strategies. The contributions include expanding the control architecture to incorporate the vision subsystem, enumerating unsafe actions for both the vision and multirotor controllers, and proposing mitigation measures such as multi-fiducial marker deployment and adaptive control techniques. The work aims to enhance reliability and safety of autonomous VTOL operations, providing a framework for resilient vision-based safety in complex flight phases.

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.

Figures (6)

• Figure 1: Illustration of an arbitrary control structure leveson2016engineering, with an expanded view of controller 1 performance. The dotted box expanded from Controller 1 represents a Nyquist plot with a gain and phase margin, indicating the performance of the controller over the frequency domain.
• Figure 2: (a) Illustration of the VTOL UAV mission phases, highlighting the take-off and landing phases as the focus of this study, with key onboard sensors and systems labeled. (b) Prototype of the VTOL UAV under development under University Leadership Initiative.
• Figure 3: Control structure for the VTOL UAV.
• Figure 4: (a) Control structure of the autopilot block. (b) Control structure of the multirotor controller block.
• Figure 5: Control structure of the Estimator and AprilTag System. The solid horizontal line indicates a mux.