A Risk Based Assessment of a small UAS Cargo Delivery Operation in Proximity to Urban Areas

Neogi, Natasha A.; Quach, Cuong C.; Dill, Evan T.

doi:10.1109/dasc.2018.8569494

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…Other recent works seek to evaluate the operational risk of particular operations, such as urban cargo delivery by small UAS [37], which focuses on the entire risk assessment for that class of operations. "A ConOps derived UAS safety risk model" develops a risk assessment model derived from concept of operations (ConOps) that uses Bayesian Belief Networks (BBN), causal narrative, and Huggins engine to determine rolled-up probability of failure for specific scenarios [38].…”

Section: Risk Framework Researchmentioning

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: Risk Framework Researchmentioning

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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“…Air traffic management services, such as aircraft separation, may then be offered in a UAS Traffic Management (UTM) inspired framework, which interfaces with today's traditional Air Traffic Management framework [17]. In this framework, authority may be delegated by the FAA to provide select air traffic management services such as low-altitude weather information, congestion management, terrain avoidance, route planning, re-rerouting, separation management, and contingency management [13,15]. One of the main attributes of the UTM system is that it does not require human operators to monitor every vehicle continuously, as in the traditional ATM system, thereby enabling increasingly autonomous realizations of specified air traffic management functions.…”

Section: Path To Deploymentmentioning

confidence: 99%

Minimum-Violation Traffic Management for Urban Air Mobility

Bharadwaj

Wongpiromsarn

Neogi

et al. 2021

Lecture Notes in Computer Science

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Urban air mobility (UAM) refers to air transportation services in and over an urban area and has the potential to revolutionize mobility solutions. However, due to the projected scale of operations, current air traffic management (ATM) techniques are not viable. Increasingly autonomous systems are a pathway to accelerate the realization of UAM operations, but must be fielded safely and efficiently. The heavily regulated, safety critical nature of aviation may lead to multiple, competing safety constraints that can be traded off based on the operational context. In this paper, we design a framework which allows for the scalable planning of a UAM ATM system. We formalize safety oriented constraints derived from FAA regulations by encoding them as temporal logic formulae. We then propose a method for UAM ATM that is both scalable and minimally violates the temporal logic constraints. Numerical results show that the runtime for our proposed algorithm is suitable for very large problems and is backed by theoretical guarantees of correctness with respect to given temporal logic constraints.

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