Terminal airspace modelling for unmanned aircraft systems integration

McFadyen, Aaron; Martin, Terrence

doi:10.1109/icuas.2016.7502622

Cited by 18 publications

(10 citation statements)

References 9 publications

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“…More recent work by NASA evaluated highthroughput helicopter route designs for airports and explored new air traffic controller communication strategies (9). The emergence of unmanned aircraft systems (UAS) also led to terminal airspace modeling in an effort to identify unused airspace in which these systems can operate non-interferingly (10).…”

Section: Background and Current Operationsmentioning

confidence: 99%

Evaluating the Interoperability of Urban Air Mobility Systems and Airports

Vascik

Hansman

2021

Transportation Research Record

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This paper investigates how existing arrival and departure procedures can be directly used or adapted to enable high-volume instrument and visual urban air mobility (UAM) flight operations at major airports in the United States. Viable procedures are restricted to those that enable simultaneous and non-interfering UAM flights with conventional aircraft operations. Air traffic controller workload is proposed as the critical integration barrier to scale UAM operations in visual conditions whereas separation minima, especially for approach procedures, is proposed as the critical barrier in instrument conditions. A systems approach is taken to evaluate potential integration strategies for UAM in which the location of UAM runways or vertipads and flight procedures are presented in a topological framework. The benefits, challenges, and notional application of five integration schemes are discussed. Four promising procedures for UAM are introduced through case studies at three airports. Findings indicate that multiple procedures exist to support high-volume UAM integration at major airports under current regulations with additional controller staffing, especially if UAM aircraft exhibit helicopter-like performance.

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Section: Background and Current Operationsmentioning

confidence: 99%

Evaluating the Interoperability of Urban Air Mobility Systems and Airports

Vascik

Hansman

2021

Transportation Research Record

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“…The mandatory nature of this system will be evident, because the current system for UAS authorizations is not scalable for the vast number of applications anticipated by government and industry [ 37 , 77 , 78 ]. New technologies onboard aircraft as well as ground support systems will need to be developed [ 13 , 79 ]. Onboard aircraft, this will mean new sensors and software tools that allows interaction and safe separation between them.…”

Section: Definition and Categorisation Of Onboard Unmanned Aircrafmentioning

confidence: 99%

Embedded Computation Architectures for Autonomy in Unmanned Aircraft Systems (UAS)

Mejias

Diguet²,

Dezan

et al. 2021

Sensors

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This paper addresses the challenge of embedded computing resources required by future autonomous Unmanned Aircraft Systems (UAS). Based on an analysis of the required onboard functions that will lead to higher levels of autonomy, we look at most common UAS tasks to first propose a classification of UAS tasks considering categories such as flight, navigation, safety, mission and executing entities such as human, offline machine, embedded system. We then analyse how a given combination of tasks can lead to higher levels of autonomy by defining an autonomy level. We link UAS applications, the tasks required by those applications, the autonomy level and the implications on computing resources to achieve that autonomy level. We provide insights on how to define a given autonomy level for a given application based on a number of tasks. Our study relies on the state-of-the-art hardware and software implementations of the most common tasks currently used by UAS, also expected tasks according to the nature of their future missions. We conclude that current computing architectures are unlikely to meet the autonomy requirements of future UAS. Our proposed approach is based on dynamically reconfigurable hardware that offers benefits in computational performance and energy usage. We believe that UAS designers must now consider the embedded system as a masterpiece of the system.

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“…We obtain air traffic data from the south east Queensland (SEQ) region in Australia from 30-11-2014 to 30-11-2015. We use the real position reports recorded by the Australian Advanced Air Traffic System (TAAATS) used for air traffic management in Australia [54]. As a pre-processing step, the data is transformed into trajectories utilising the aircraft identification tags and the reported timing.…”

Section: Experiments 1: Terminal Area Air Traffic Predictionmentioning

confidence: 99%

Tree Memory Networks for modelling long-term temporal dependencies

et al. 2018

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In the domain of sequence modelling, Recurrent Neural Networks (RNN) have been capable of achieving impressive results in a variety of application areas including visual question answering, part-of-speech tagging and machine translation. However this success in modelling short term dependencies has not successfully transitioned to application areas such as trajectory prediction, which require capturing both short term and long term relationships. In this paper, we propose a Tree Memory Network (TMN) for jointly modelling both long term relationships between multiple sequences and short term relationships within a sequence, in sequence-to-sequence mapping problems. The proposed network architecture is composed of an input module, controller and a memory module. In contrast to related literature which models the memory as a sequence of historical states, we model the memory as a recursive tree structure. This structure more effectively captures temporal dependencies across both short and long term time periods through its hierarchical structure. We demonstrate the effectiveness and flexibility of the proposed TMN in two practical problems: aircraft trajectory modelling and pedestrian trajectory modelling in a surveillance setting. In both cases the proposed approach outperforms the current state-of-the-art. Furthermore, we perform an in depth analysis on the evolution of the memory module content over time and provide visual evidence on how the proposed TMN is able to map both short and long term relationships efficiently via a hierarchical structure.

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Terminal airspace modelling for unmanned aircraft systems integration

Cited by 18 publications

References 9 publications

Evaluating the Interoperability of Urban Air Mobility Systems and Airports

Evaluating the Interoperability of Urban Air Mobility Systems and Airports

Embedded Computation Architectures for Autonomy in Unmanned Aircraft Systems (UAS)

Tree Memory Networks for modelling long-term temporal dependencies

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