TY - GEN
T1 - Integrating Vision Systems and STPA for Robust Landing and Take-Off in VTOL Aircraft
AU - Banik, Sandeep
AU - Kim, Jinrae
AU - Hovakimyan, Naira
AU - Carlone, Luca
AU - Thomas, John P.
AU - Leveson, Nancy G.
N1 - This material is based upon work supported by the National Aeronautics and Space Administration (NASA) under the cooperative agreement 80NSSC20M0229, University Leadership Initiative grant no. 80NSSC22M0070 and Air Force Office of Scientific Research (AFOSR) grant no. FA9550-21-1-0411. This work was also supported by the AFOSR \u201CCertifiable and Self-Supervised Category-Level Tracking\u201D program, Carlone\u2019s NSF CAREER award. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
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U2 - 10.2514/6.2025-1324
DO - 10.2514/6.2025-1324
M3 - Conference contribution
AN - SCOPUS:86000007466
SN - 9781624107238
T3 - AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025
BT - AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025
Y2 - 6 January 2025 through 10 January 2025
ER -