Multi-Agent Autonomous Operations in Urban Air Mobility with Communication Constraints

Yang, Xuxi; Deng, Liping; Liu, Jia; Wei, Peng; Li, Husheng

doi:10.2514/6.2020-1839

Cited by 17 publications

(6 citation statements)

References 27 publications

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“…Such a threshold can be chosen based on an estimate of communication latency from available historical data or from a model. Latency of a specific channel can be mathematically modelled [63,64,65] or predicted using statistical analysis. Also, the threshold can be determined from available data of the specific aircraft transmitter-receiver.…”

Section: Design Of the Control Allocation Agentmentioning

confidence: 99%

Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

Tabassum,

Bai

2021

Preprint

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This paper develops and evaluates the performance of an allocation agent to be potentially integrated into the onboard Detect and Avoid (DAA) computer of an Unmanned Aircraft System (UAS). We consider a UAS that can be fully controlled by the onboard DAA system and by a remote human pilot. With a communication channel prone to latency, we consider a mixed initiative interaction environment, where the control authority of the UAS is dynamically allocated by the allocation agent. In an encounter with a dynamic intruder, the probability of collision may increase in the absence of pilot commands in the presence of latency. Moreover, a delayed pilot command may not result in safe resolution of the current scenario and need to be improvised. We design an optimization algorithm to reduce collision risk and refine delayed pilot commands. Towards this end, a Markov Decision Process (MDP) and its solution are employed to create a wait time map. The map consists of estimated times that the UAS can wait for the remote pilot commands at each state. A command blending algorithm is designed to select an avoidance maneuver that prioritizes the pilot intention extracted from the pilot commands. The wait time map and the command blending algorithm are implemented and integrated into a closed-loop simulator. We conduct ten thousands fast-time Monte Carlo simulations and compare the performance of the integrated setup with a standalone DAA setup. The simulation results show that the allocation agent enables the UAS to wait without inducing any near mid air collision (NMAC) and severe loss of well clear (LoWC) while positively improve pilot involvement in the encounter resolution.

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Section: Design Of the Control Allocation Agentmentioning

confidence: 99%

Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

Tabassum,

Bai

2021

Preprint

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“…The quantitative NMAC definition has not been adopted universally, however. The policy definition of a loss of separation of 500 feet was used decades ago by Castro [40], Shuch [41], Gifford and Sinha [42], and more recently by Yang et al [43]. For emerging research regarding s mall UAS and urban air mobility (UAM) concepts, non-traditional NMAC definitions have been proposed.…”

Section: B Use Of Nmac To Support Development Of Collision Avoidancementioning

confidence: 99%

A Quantitatively Derived NMAC Analog for Smaller Unmanned Aircraft Systems Based on Unmitigated Collision Risk

Weinert¹,

Alvarez²,

Owen³

et al. 2020

Preprint

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The capability to avoid other air traffic is a fundamental component of the layered conflict management system to ensure safe and efficient operations in the National Airspace System. The evaluation of systems designed to mitigate the risk of midair collisions of manned aircraft are based on large-scale modeling and simulation efforts and a quantitative volume defined as a near midair collision (NMAC). Since midair collisions are difficult to observe in simulation and are inherently rare events, basing evaluations on NMAC enables a more robust statistical analysis. However, an NMAC and its underlying assumptions for assessing close encounters with manned aircraft do not adequately consider the different characteristics of smaller UAS-only encounters. The primary contribution of this paper is to explore quantitative criteria to use when simulating two or more smaller UASs in sufficiently close proximity that a midair collision might reasonably occur and without any mitigations to reduce the likelihood of a midair collision. The criteria assumes a historically motivated upper bound for the collision likelihood and subsequently identify the smallest possible NMAC analogs. We also demonstrate the NMAC analogs can be used to support modeling and simulation activities.

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“…Nowadays, the deployment of urban air mobility (UAM), which transports cargo and passengers in a city, is actively considered [18]. Most of the conventional studies of NOMA-based aerial systems consider a UAV as an aerial user (AU).…”

Section: Introductionmentioning

confidence: 99%

Link Balance and Performance of UAM in NOMA-based Cellular Networks

2022

IJIES

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Recently, the non-orthogonal multiple access (NOMA) technique, which transmits multiple users in the same time-frequency block, has been focused on for future cellular networks. Conventional NOMA-based aerial cellular networks consider an unmanned aerial vehicle (UAV) as an aerial user, in which secure control information for UAV is important. In recent days, the deployment of urban air mobility (UAM) has been actively discussed for a new aerial transport service in an urban area. Since UAM conveys passengers, both the traffic and the control information for passengers and a vehicle are important. Therefore, this paper considers a NOMA-based aerial cellular network with a UAM as an aerial user and a ground user as a terrestrial user. And we derive closed-form outage probabilities of the forward and reverse links of UAM, and the Monet-Carlo simulation verifies the results. Further, the condition to maintain the balance between the forward and the reverse links for the identical quality of service (QoS) is derived. Numerical results show that the outage probability of the forward link decreases with the increase of the received signal-to-noise ratio (SNR). However, we noticed that the outage probability of the reverse link shows the error floor, which does not decrease with the received SNR and remains constant, caused by the interference from the ground user (GU). Also, it is shown that there is an average transmit power of UAM to conform the link balance regardless of strong or weak users.

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Multi-Agent Autonomous Operations in Urban Air Mobility with Communication Constraints

Cited by 17 publications

References 27 publications

Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

A Quantitatively Derived NMAC Analog for Smaller Unmanned Aircraft Systems Based on Unmitigated Collision Risk

Link Balance and Performance of UAM in NOMA-based Cellular Networks

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