Design Considerations for Safer Small UAS

Logan, Michael J.; Gundlach, Jay; Vranas, Thomas L.

doi:10.2514/6.2018-1724

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…For small UAS, fault detection is often difficult as there are not always backup or monitoring systems available to detect or handle subsystem failures. Redundancies and detection for many aspects of small UAS, including IMU, can be provided if designed properly [62]. In "Fault Detection, Identification and Accommodation Techniques for Unmanned Airborne Vehicle", Lennon R. Cork, et al [8] proposed using a neural network to detect abnormal operation of the IMU and to replace the IMU data with output from the neural network should such a detection occur.…”

Section: Fault Detection Identification and Accommodation (Fdia)mentioning

confidence: 99%

Application of Dempster–Shafer Networks to a Real-Time Unmanned Systems Risk Analysis Framework

Dunham

Johnson

Féron

et al. 2021

Journal of Aerospace Information Systems

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Unmanned Aerial Systems (UASs) are continuing to proliferate rapidly [1]. Quantitative risk assessment for UAS operations, both a-priori and during the operation, are necessary for governing authorities and insurance companies to understand the risks and properly approve operations and assign insurance premiums, respectively. This paper reports the results of the 2018 UAS Safety Symposium held at the Georgia Institute of Technology, which was conducted as part of this research. This symposium was comprised of experts in UAS law, insurance, regulations, operations, and research discussing the direction of the UAS industry and the necessary steps to move the industry forward. Those results motivate the remainder of this research including the novel application of Dempster-Shafer networks using auto-updating transition matrices to the problem of UAS risk analysis and decision-making. This research trains a DS network based on simulated operation data, tests the capabilities of the trained network to make real-time decisions on a small UAS against a baseline system in a representative mission, and explores how this system would extend to the full UAS ecosystem as discussed in the 2018 UAS safety symposium. Conclusions are drawn with respect to the research performed, and additional research tangents are proposed.

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Section: Fault Detection Identification and Accommodation (Fdia)mentioning

confidence: 99%

Application of Dempster–Shafer Networks to a Real-Time Unmanned Systems Risk Analysis Framework

Dunham

Johnson

Féron

et al. 2021

Journal of Aerospace Information Systems

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“…From this concept, a new standard has emerged through the Pixhawk architecture (mirrored by companies such as Emlid [45] and Aerotenna [44]) that include multiply redundant IMUs with underlying software capable of choosing the most reliable output for use by the flight computer [65,66]. This method aligns with research on redundancy and detection by design [67]. Similarly, fault detection methods for air data [68] have been developed that do not required hardware redundancy, which work well for small general aviation and UAS aircraft.…”

Section: Fault Detectionmentioning

confidence: 99%

Avionics of Aerial Robots

et al. 2021

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Purpose of Review This work provides an overview of avionic systems, which can be used as an entry point to learn about their architecture and components, and as a guide for studying the recent development of unmanned aerial systems avionics. Recent FindingsThe development trend of avionics for unmanned aerial systems tends toward the one used for a large aircraft both in the structure of the architecture and in design and implementation processes. However, due to the differences in the operational environment, challenges remain in specifying the proper requirements, and proving their safety and security. SummaryThe paper reviews the functionalities and importance of avionic systems, especially in unmanned aerial systems. It also provides the historical background and the future trend of system development in terms of safety, security, and certification for the purpose of integrating these unmanned aerial systems into an urban airspace.

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“…Ref. [21] recommends battery failures be mitigated by employing batteries in parallel. This paper proposes a battery reconfiguration Markov Decision Process (MDP) for a series-parallel battery pack as shown in Figure 1.…”

Section: Problem Statementmentioning

confidence: 99%

Prognostics-Informed Battery Reconfiguration in a Multi-Battery Small UAS Energy System

Sharma¹,

Atkins²

2021

Preprint

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Batteries have been identified as one most likely small UAS (sUAS) components to fail in flight. sUAS safety will therefore be improved with redundant or backup batteries. This paper presents a prognostics-informed Markov Decision Process (MDP) model for managing multi-battery reconfiguration for sUAS missions. Typical lithium polymer (Lipo) battery properties are experimentally characterized and used in Monte Carlo simulations to establish battery dynamics in sUAS flights of varying duration. Case studies illustrate the trade off between multi-battery system increased complexity/weight and resilience to non-ideal battery performance.

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Design Considerations for Safer Small UAS

Cited by 4 publications

References 1 publication

Application of Dempster–Shafer Networks to a Real-Time Unmanned Systems Risk Analysis Framework

Application of Dempster–Shafer Networks to a Real-Time Unmanned Systems Risk Analysis Framework

Avionics of Aerial Robots

Prognostics-Informed Battery Reconfiguration in a Multi-Battery Small UAS Energy System

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