Small Unmanned Aircraft System (sUAS) Trajectory Modeling in Support of UAS Traffic Management (UTM)

Ren, Liling; Castillo-Effen, Mauricio; Han, Yu; Johnson, Eric S.; Takuma, Nakamura; Yoon, Yongeun; Ippolito, Corey A.

doi:10.2514/6.2017-4268

Cited by 18 publications

(6 citation statements)

References 16 publications

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“…For all our experiments we used a simulator designed for Small Unmanned Aircraft System (sUAS) designed as a part of the UAS traffic management system project [16]. This software package is intended to simulate operations of small unmanned aircraft systems weighing less than 55 lb.…”

Section: Methodsmentioning

confidence: 99%

“…Unmanned aircraft of this size have been flown as model aircraft for recreation and sports uses for many decades. Wide-spread use of sUAS for non-recreational purposes has been limited, particularly for applications requiring operations beyond the visual line of sight (BVLOS) from the operator [16]. However, driven by the potential societal and economic benefits that can be generated from the use of sUAS, new systems such as RTSA can enable civilian low-altitude airspace unmanned aircraft operations, particularly sUAS BVLOS operations by providing airspace corridors or geofences.…”

Section: Methodsmentioning

confidence: 99%

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Runtime Safety Assurance Using Reinforcement Learning

Lazarus¹,

Lopez²,

Kochenderfer³

2020

Preprint

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The airworthiness and safety of a non-pedigreed autopilot must be verified, but the cost to formally do so can be prohibitive. We can bypass formal verification of non-pedigreed components by incorporating Runtime Safety Assurance (RTSA) as mechanism to ensure safety. RTSA consists of a metacontroller that observes the inputs and outputs of a non-pedigreed component and verifies formally specified behavior as the system operates. When the system is triggered, a verified recovery controller is deployed. Recovery controllers are designed to be safe but very likely disruptive to the operational objective of the system, and thus RTSA systems must balance safety and efficiency. The objective of this paper is to design a metacontroller capable of identifying unsafe situations with high accuracy. High dimensional and non-linear dynamics in which modern controllers are deployed along with the black-box nature of the nominal controllers make this a difficult problem. Current approaches rely heavily on domain expertise and human engineering. We frame the design of RTSA with the Markov decision process (MDP) framework and use reinforcement learning (RL) to solve it. Our learned meta-controller consistently exhibits superior performance in our experiments compared to our baseline, human engineered approach.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Runtime Safety Assurance Using Reinforcement Learning

Lazarus¹,

Lopez²,

Kochenderfer³

2020

Preprint

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“…Also, the conception and evaluation of In-Flight CDR methods with simulations based on real world scenarios is discussed in [10], [11]. [16] introduce 4DT (3D plus time Trajectories) into UAV trajectory modeling, and they focus on the uncertainties and technical errors in UAV navigation. However, none of these works have presented practical methods to proactively de-conflict UAV traffic before the UAVs take off, i.e., Pre-Flight CDR methods.…”

Section: Related Work a Conflict Detection And Resolution Methomentioning

confidence: 99%

“…• A speed sp i : the given speed of a i is considered uniform on the whole path. In the UTM context, this would be a constraint to ensure conflict-free paths generation [16];…”

Section: A Pre-flight Cdr Problem Modelmentioning

confidence: 99%

Pre-Flight Conflict Detection and Resolution for UAV Integration in Shared Airspace: Sendai 2030 Model Case

Geraldes

Gonçalves

et al. 2019

IEEE Access

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The increasing demand for services performed by Unmanned Aerial Vehicles (UAVs) requires the simulation of Unmanned Aircraft System Traffic Management (UTM) systems. In particular, Pre-Flight Conflict Detection and Resolution (CDR) methods need to scale to future demand levels and generate conflict-free paths for a potentially large number of UAVs before actual takeoff. However, few studies have examined realistic scenarios and the requirements for the UTM system. In this paper, we focus on the Sendai 2030 model case, a realistic projection of UAV usage for deliveries in one area in Japan. This model case considers up to 21,000 requests for Unmanned Aircraft Systems (UAS) operations over a 13 hour service time, and thus poses a challenge for the Pre-Flight CDR methods. Therefore, we propose an airspace reservation method based on 4DT (3D plus time Trajectories) and map the Pre-Flight CDR problem to a Multi-Agent Path Finding (MAPF) problem. We study first-come first-served (FCFS) and ''batch'' processing of UAS operation requests, and compare the throughput of those methods. We analyze the air traffic topology of deliveries by UAVs, and discuss several metrics to better understand the complexity of air traffic in the Sendai model case. INDEX TERMS Unmanned aircraft system traffic management (UTM), pre-flight conflict detection and resolution (CDR), multi-agent path finding (MAPF), air traffic complexity metrics.

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“…However, no results on TCL 3 and TCL 4 have been reported, to the best of our knowledge. On the other hand, a few works have conceptually discussed the architecture of UTM system and identified its basic elements [6][7][8]. These works envision UTM based on the existing Air Traffic Management (ATM) system for crewed aircraft.…”

Section: Introductionmentioning

confidence: 99%

Control Protocol Design and Analysis for Unmanned Aircraft System Traffic Management

Zhou

Sun

Hwang

et al. 2020

Preprint

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Due to the rapid development technologies for small unmanned aircraft systems (sUAS), the supply and demand market for sUAS is expanding globally. With the great number of sUAS ready to fly in civilian airspace, an sUAS aircraft traffic management system that can guarantee the safe and efficient operation of sUAS is still at absence. In this paper, we propose a control protocol design and analysis method for sUAS traffic management (UTM) which can safely manage a large number of sUAS. The benefits of our approach are two folds: at the top level, the effort for monitoring sUAS traffic (authorities) and control/planning for each sUAS (operator/pilot) are both greatly reduced under our framework; and at the low level, the behavior of individual sUAS is guaranteed to follow the restrictions. Mathematical proofs and numerical simulations are presented to demonstrate the proposed method.

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Small Unmanned Aircraft System (sUAS) Trajectory Modeling in Support of UAS Traffic Management (UTM)

Cited by 18 publications

References 16 publications

Runtime Safety Assurance Using Reinforcement Learning

Runtime Safety Assurance Using Reinforcement Learning

Pre-Flight Conflict Detection and Resolution for UAV Integration in Shared Airspace: Sendai 2030 Model Case

Control Protocol Design and Analysis for Unmanned Aircraft System Traffic Management

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