集成视觉系统与STPA以实现VTOL飞行器的稳健起降
Integrating Vision Systems and STPA for Robust Landing and Take-Off in VTOL Aircraft
AI总结：
针对VTOL无人机在不确定动态环境下起降的安全挑战，提出结合视觉传感器融合与STPA的集成方法，通过基准标记与危害分析识别不安全控制动作并提出缓解策略，提升作业可靠性与安全性。
摘要：
垂直起降（VTOL）无人机（UAV）是用途广泛的平台，被广泛应用于监视、搜救以及城市空中交通等领域。尽管具备应用潜力，但受环境不确定性、传感器噪声以及系统级交互影响，在不确定动态环境中执行起降这一关键阶段仍存在重大安全挑战。本文提出一种集成方法，将基于视觉的传感器融合与系统理论过程分析（STPA，System-Theoretic Process Analysis）相结合，以提升VTOL无人机起降作业的安全性与稳健性。通过将AprilTag等基准标记（fiducial marker）纳入控制架构，并开展全面的危害分析，我们识别出不安全控制动作并提出缓解策略。核心贡献包括：开发搭载可识别基准标记的视觉系统的控制结构、多旋翼控制器，以及对应的不安全控制动作与缓解策略。所提方案有望提升VTOL无人机作业的可靠性与安全性，为构建高韧性自主系统铺平道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
