Unmanned aerial vehicles or drones are widely used or proposed to carry out various tasks in low-altitude airspace. To safely integrate drone traffic into congested airspace, the current concept of operations for drone traffic management will reserve a static traffic volume for the whole planned trajectory, which is safe but inefficient. In this paper, we propose a dynamic traffic volume reservation method for the drone traffic management system based on a multiscale A* algorithm. The planning airspace is represented as a multiresolution grid world, where the resolution will be coarse for the area on the far side. Therefore, each drone only needs to reserve a temporary traffic volume along the finest flight path in its local area, which helps release the airspace back to others. Moreover, the multiscale A* can run nearly in real-time due to a much smaller search space, which enables dynamically rolling planning to consider updated information. To handle the infeasible corner cases of the multiscale algorithm, a hybrid strategy is further developed, which can maintain a similar optimal level to the classic A* algorithm while still running nearly in real-time. The presented numerical results support the advantages of the proposed approach.
more »
« less
Time- and space-parallel simulation of air traffic networks
Computer simulations are widely used to design and evaluate air traffic systems. A fast time simulation capability is essential to effectively explore the consequences of decisions in airspace design, air traffic management, and operations. A parallel simulation approach is proposed to accelerate fast time simulation of air traffic networks that exploits both temporal and spatial parallelisms. A time-parallel algorithm is first described that simulates different time intervals concurrently and uses a fix up computation that exploits the scheduled nature of commercial air traffic to address the problem of dependencies between time segments. The time-parallel algorithm is then extended with a space-parallel simulation approach using Time Warp to simulate each time segment in parallel thereby increasing the amount of parallelism that can be exploited. The time and space-parallel algorithms are evaluated using a simulation of the U.S. National Airspace System (NAS). Experimental data is presented demonstrating that this approach can achieve greater acceleration than what can be achieved by exploiting time-parallel or space-parallel simulation techniques alone.
more » « less- PAR ID:
- 10547058
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- SIMULATION
- Volume:
- 95
- Issue:
- 12
- ISSN:
- 0037-5497
- Format(s):
- Medium: X Size: p. 1213-1228
- Size(s):
- p. 1213-1228
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Cristina Ceballos (Ed.)The current National Airspace System (NAS) is reaching capacity due to increased air traffic, and is based on outdated pre-tactical planning. This study proposes a more dynamic airspace configuration (DAC) approach that could increase throughput and accommodate fluctuating traffic, ideal for emergencies. The proposed approach constructs the airspace as a constraints-embedded graph, compresses its dimensions, and applies a spectral clustering-enabled adaptive algorithm to generate collaborative airport groups and evenly distribute workloads among them. Under various traffic conditions, our experiments demonstrate a 50% reduction in workload imbalances. This research could ultimately form the basis for a recommendation system for optimized airspace configuration. Code available at https://github.com/KeFenge2022/GraphDAC.gitmore » « less
-
The key concept for safe and efficient traffic management for Unmanned Aircraft Systems (UAS) is the notion of operation volume (OV). An OV is a 4-dimensional block of airspace and time, which can express an aircraft’s intent, and can be used for planning, de-confliction, and traffic management. While there are several high-level simulators for UAS Traffic Management (UTM), we are lacking a frame- work for creating, manipulating, and reasoning about OVs for heterogeneous air vehicles. In this paper, we address this and present SkyTrakx—a software toolkit for simulation and verification of UTM scenarios based on OVs. First, we illustrate a use case of SkyTrakx by presenting a specific air traffic coordination protocol. This protocol communicates OVs between participating aircraft and an airspace manager for traffic routing. We show how existing formal verification tools, Dafny and Dione, can assist in automatically checking key properties of the protocol. Second, we show how the OVs can be computed for heterogeneous air vehicles like quadcopters and fixed-wing aircraft using another verification technique, namely reachability analysis. Finally, we show that SkyTrakx can be used to simulate complex scenarios involving heterogeneous vehicles, for testing and performance evaluation in terms of workload and response delays analysis. Our experiments delineate the trade-off between performance and workload across different strategies for generating OVs.more » « less
-
This paper develops a decision framework to automate the playbook for UAS traffic management (UTM) under uncertain environmental conditions based on spatiotemporal scenario data. Motivated by the traditional air traffic management (ATM) which uses the playbook to guide traffic using pre-validated routes under convective weather, the proposed UTM playbook leverages a database to store optimal UAS routes tagged with spatiotemporal wind scenarios to automate the UAS trajectory management. Our perspective is that the UASs, and many other modern systems, operate in spatiotemporally evolving environments, and similar spatiotemporal scenarios are tied with similar management decisions. Motivated by this feature, our automated playbook solution integrates the offline operations, online operations and a database to enable real-time UAS trajectory management decisions. The solution features the use of similarity between spatiotemporal scenarios to retrieve offline decisions as the initial solution for online fine tuning, which significantly shortens the online decision time. A fast query algorithm that exploits the correlation of spatiotemporal scenarios is utilized in the decision framework to quickly retrieve the best offline decisions. The online fine tuning adapts to trajectory deviations and subject to collision avoidance among UASs. The solution is demonstrated using simulation studies, and can be utilized in other applications, where quick decisions are desired and spatiotemporal environments play a crucial role in the decision process.more » « less
-
more » « less
Abstract Urban air mobility (UAM) is an emerging air transportation mode to alleviate the ground traffic burden and achieve zero direct aviation emissions. Due to the potential economic scaling effects, the UAM traffic flow is expected to increase dramatically once implemented, and its market can be substantially large. To be prepared for the era of UAM, we study the fair and risk‐averse urban air mobility resource allocation model (FairUAM) under passenger demand and airspace capacity uncertainties for fair, safe, and efficient aircraft operations. FairUAM is a two‐stage model, where the first stage is the aircraft resource allocation, and the second stage is to fairly and efficiently assign the ground and airspace delays to each aircraft provided the realization of random airspace capacities and passenger demand. We show that FairUAM is NP‐hard even when there is no delay assignment decision or no aircraft allocation decision. Thus, we recast FairUAM as a mixed‐integer linear program (MILP) and explore model properties and strengthen the model formulation by developing multiple families of valid inequalities. The stronger formulation allows us to develop a customized exact decomposition algorithm with both benders and L‐shaped cuts, which significantly outperforms the off‐the‐shelf solvers. Finally, we numerically demonstrate the effectiveness of the proposed method and draw managerial insights when applying FairUAM to a real‐world network.
