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.

翻译：垂直起降无人机作为多功能平台，在监视、搜救及城市空中交通等领域具有广泛应用前景。然而，在不确定动态环境中的起降关键阶段，由于环境不确定性、传感器噪声及系统级交互作用，其安全性面临严峻挑战。本文提出一种融合视觉传感器与系统理论过程分析的综合方法，以增强VTOL无人机在起降阶段的安全性与鲁棒性。通过将AprilTags等基准标记物纳入控制架构，并执行系统性危害分析，我们识别出不安全控制行为并提出相应缓解策略。核心贡献包括：开发具备基准标记识别能力的视觉系统控制架构、多旋翼控制器，以及对应的不安全控制行为识别与缓解策略。所提方案有望提升VTOL无人机作业的可靠性与安全性，为构建强韧的自主系统奠定基础。