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Drones
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Unmanned Traffic Management Systems
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Unmanned Traffic Management Systems

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 25 March 2025 | Viewed by 11616

Special Issue Editors

Dr. Aaron Mcfadyen

E-Mail Website
Guest Editor
Faculty of Engineering (FoE), Electrical Engineering and Robotics (EER), Brisbane, Australia
Interests: unmanned traffic management; risk analysis; control
Dr. Luis Mejias

E-Mail Website
Guest Editor
Faculty of Engineering (FoE), Electrical Engineering and Robotics (EER), Brisbane, Australia
Interests: vision-based control; collision avoidance; navigation and control

Special Issue Information

Dear Colleagues,

Unmanned Traffic Management (UTM) describes a new traffic management ecosystem that will safely coordinate low-altitude drone operations. Unlike Air Traffic Management (ATM), UTM relies on sharing information via digital interfaces and highly automated distributed systems to manage drone operations. This represents a huge shift in airspace management but is a key enabler for drone operators, authorities and the businesses relying on drone services. The main challenge is to create the technologies, procedures and services for a resilient, scalable and sustainable UTM that can integrate with the current aviation ecosystem.

This Special Issue aims at collecting new developments, methodologies, best practices and innovations in Unmanned Traffic Management (UTM). We welcome submissions that provide the most recent advancements on all aspects of UTM, including, but not limited to:

Suggested themes and article types for submissions:

  • Airspace Management (inc. design, modelling and optimisation);
  • Automated Services (inc. authorisation, flight planning, coordination and flow management, conformance);
  • Drone Navigation, Surveillance and Communication (inc. performance-based approaches);
  • Strategic and Tactical Mitigation Services;
  • Data and Information Management (inc. storage, exchange/sharing and visualisation);
  • Risk, Safety and Regulation.

Dr. Aaron Mcfadyen
Dr. Luis Mejias
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • unmanned traffic management
  • flight information management
  • integrated traffic management
  • air traffic management
  • unmanned aircraft
  • communications, navigation and surveillance (CNS)
  • traffic flow management
  • collision mitigation
  • risk analysis

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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Research

16 pages, 8966 KiB  
Article
Airspace Constrained Free-Flight Analysis: Implications for Uncrewed Air Traffic Management
by Troy Bruggemann, Aaron McFadyen and Brendan Williams
Drones 2024, 8(10), 603; https://doi.org/10.3390/drones8100603 - 21 Oct 2024
Viewed by 738
Abstract
This paper provides a study of free-flight air traffic behaviour in increasingly constrained airspace environments. Traffic assumes three different free-flight operational constructs with airspace constraints considered as restricted (no-fly) regions. Simulations combine path planning and Monte Carlo techniques to qualitatively analyse emergent traffic [...] Read more.
This paper provides a study of free-flight air traffic behaviour in increasingly constrained airspace environments. Traffic assumes three different free-flight operational constructs with airspace constraints considered as restricted (no-fly) regions. Simulations combine path planning and Monte Carlo techniques to qualitatively analyse emergent traffic behaviour and quantitatively assess spatial–temporal airspace conflict as the airspace constraints vary. Findings indicate that airspace constraints have a much stronger influence on aircraft behaviour than the free-flight operational construct, with any benefits of free flight rapidly diminishing as the airspace becomes more constrained. We conclude that structured traffic route (or network) designs and associated risk modelling approaches should be considered for safe and efficient traffic management of highly constrained and congested (or dense) airspace. This work therefore provides evidence to inform new airspace design and management initiatives, including low-altitude uncrewed traffic. Full article
(This article belongs to the Special Issue Unmanned Traffic Management Systems)
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20 pages, 6627 KiB  
Article
Comprehensive Task Optimization Architecture for Urban UAV-Based Intelligent Transportation System
by Marco Rinaldi and Stefano Primatesta
Drones 2024, 8(9), 473; https://doi.org/10.3390/drones8090473 - 10 Sep 2024
Cited by 2 | Viewed by 1473
Abstract
This paper tackles the problem of resource sharing and dynamic task assignment in a task scheduling architecture designed to enable a persistent, safe, and energy-efficient Intelligent Transportation System (ITS) based on multi-rotor Unmanned Aerial Vehicles (UAVs). The addressed task allocation problem consists of [...] Read more.
This paper tackles the problem of resource sharing and dynamic task assignment in a task scheduling architecture designed to enable a persistent, safe, and energy-efficient Intelligent Transportation System (ITS) based on multi-rotor Unmanned Aerial Vehicles (UAVs). The addressed task allocation problem consists of heterogenous pick-up and delivery tasks with time deadline constraints to be allocated to a heterogenous fleet of UAVs in an urban operational area. The proposed architecture is distributed among the UAVs and inspired by market-based allocation algorithms. By exploiting a multi-auctioneer behavior for allocating both delivery tasks and re-charge tasks, the fleet of UAVs is able to (i) self-balance the utilization of each drone, (ii) assign dynamic tasks with high priority within each round of the allocation process, (iii) minimize the estimated energy consumption related to the completion of the task set, and (iv) minimize the impact of re-charge tasks on the delivery process. A risk-aware path planner sampling a 2D risk map of the operational area is included in the allocation architecture to demonstrate the feasibility of deployment in urban environments. Thanks to the message exchange redundancy, the proposed multi-auctioneer architecture features improved robustness with respect to lossy communication scenarios. Simulation results based on Monte Carlo campaigns corroborate the validity of the approach. Full article
(This article belongs to the Special Issue Unmanned Traffic Management Systems)
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16 pages, 5365 KiB  
Article
Characterization of Strategic Deconflicting Service Impact on Very Low-Level Airspace Capacity
by Zhiqiang Liu, Jose Luis Munoz-Gamarra and Juan José Ramos Gonzalez
Drones 2024, 8(9), 426; https://doi.org/10.3390/drones8090426 - 25 Aug 2024
Cited by 1 | Viewed by 792
Abstract
European airspace is poised for significant transformation as it prepares to accommodate a new class of unmanned traffic that will reshape the transport of people and goods. Unmanned aerial vehicle traffic will introduce a new level of services, but it remains unclear how [...] Read more.
European airspace is poised for significant transformation as it prepares to accommodate a new class of unmanned traffic that will reshape the transport of people and goods. Unmanned aerial vehicle traffic will introduce a new level of services, but it remains unclear how safety and operators’ time flexibility in flight planning will impact capacity. This study focuses on the impact of strategic deconflicting services on the capacity of the very low-level airspace, a critical area in the future management of unmanned aerial vehicle traffic. The results validate the assumptions regarding the roles of airspace managers and drone operators through simulation studies; highlight the limitations of the first come, first served policy; and propose a batch policy as a potential optimization strategy for future airspace capacity management. The forecasting model developed using regression techniques provides a general method for predicting airspace capacity under specific conditions, contributing to the safe and efficient integration of unmanned aerial vehicles into European airspace. Full article
(This article belongs to the Special Issue Unmanned Traffic Management Systems)
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21 pages, 1910 KiB  
Article
A Hamilton–Jacobi Reachability-Based Minimum Separation Estimation of Integrated Manned and Unmanned Operation in Uncertain Environments
by Maolin Wang, Renli Lv and Shang Tai
Drones 2024, 8(7), 278; https://doi.org/10.3390/drones8070278 - 21 Jun 2024
Viewed by 856
Abstract
This work presents a minimum separation calculation for the integrated operation of manned and unmanned aerial vehicles in an uncertain airspace environment. Different from traditional path-planning-based research, this study investigated the minimum safe separation distance from a novel perspective of reachability analysis. The [...] Read more.
This work presents a minimum separation calculation for the integrated operation of manned and unmanned aerial vehicles in an uncertain airspace environment. Different from traditional path-planning-based research, this study investigated the minimum safe separation distance from a novel perspective of reachability analysis. The proposed computational method made use of the Hamilton–Jacobi partial differential equation (HJPDE) to obtain the backward reachable tube. Firstly, this work modeled the integrated operation in the UAS traffic management scenario, particularly focusing on the uncertainties. Then, a probabilistic reachability tube computation method was derived. Next, this work calculated the safe separation distances based on reachability analysis for three scenarios: a deterministic environment, an environment with relative position uncertainty, and an environment with relative heading angle uncertainty. By calculating the reachable tubes for a given response time, the worst-case minimum safe distances from the UAV’s perspective were determined, and the quantitative patterns were summarized. The results in this work indicate that, with an increase in the risk level and under the premise of a 1 s response time, the minimum safe separation increases from 26.7 m to 30.0 m. Finally, the paper discusses the results, explaining their rationality from both mathematical and physical perspectives. Full article
(This article belongs to the Special Issue Unmanned Traffic Management Systems)
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29 pages, 5681 KiB  
Article
U-Space Utilisation of Airspace under Various Layer Function Assignments and Allocations
by Andres Morfin Veytia, Calin Andrei Badea, Niki Patrinopoulou, Ioannis Daramouskas, Joost Ellerbroek, Vaios Lappas, Vassilios Kostopoulos and Jacco Hoekstra
Drones 2023, 7(7), 444; https://doi.org/10.3390/drones7070444 - 5 Jul 2023
Cited by 1 | Viewed by 1445
Abstract
The interest in urban air mobility as a potential solution for urban congestion is steadily growing. Air operations in urban areas can present added complexity as compared with traditional air traffic management. As a result, it is necessary to test and develop novel [...] Read more.
The interest in urban air mobility as a potential solution for urban congestion is steadily growing. Air operations in urban areas can present added complexity as compared with traditional air traffic management. As a result, it is necessary to test and develop novel airspace designs and rules. As airspace in urban areas is a scarce resource, creating structures and rules that effectively utilise the airspace is an important challenge. This work specifically focuses on layered airspace design in urban operations constrained to fly between the existing buildings. Two design parameters of airspace design are investigated with two sub-experiments. Sub-experiment 1 investigates layer function assignment by comparing concepts from previous research with different layer assignment distributions. Sub-experiment 2 investigates the flight rules of vertical distribution of traffic within the airspace, to determine whether this is best achieved in a static (pre-allocated) or dynamic manner. Both sub-experiments analyse the overall system safety, route duration, and route distance under increasing traffic demand. Results reveal that the importance of cruising airspace is apparent at high densities. Results also shows that the safest layer allocation flight rule depends on the traffic density. At lower densities dynamic rules help to spread traffic locally. However, when the airspace is saturated it is safer to pre-allocate flight heights if achieved uniformly. Full article
(This article belongs to the Special Issue Unmanned Traffic Management Systems)
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19 pages, 8368 KiB  
Article
Dynamic Capacity Management for Air Traffic Operations in High Density Constrained Urban Airspace
by Niki Patrinopoulou, Ioannis Daramouskas, Calin Andrei Badea, Andres Morfin Veytia, Vaios Lappas, Joost Ellerbroek, Jacco Hoekstra and Vassilios Kostopoulos
Drones 2023, 7(6), 395; https://doi.org/10.3390/drones7060395 - 14 Jun 2023
Cited by 3 | Viewed by 2301
Abstract
Unmanned Aircraft Systems (UAS) Traffic Management (UTM) is an active research subject as its proposed applications are increasing. UTM aims to enable a variety of UAS operations, including package delivery, infrastructure inspection, and emergency missions. That creates the need for extensive research on [...] Read more.
Unmanned Aircraft Systems (UAS) Traffic Management (UTM) is an active research subject as its proposed applications are increasing. UTM aims to enable a variety of UAS operations, including package delivery, infrastructure inspection, and emergency missions. That creates the need for extensive research on how to incorporate such traffic, as conventional methods and operations used in Air Traffic Management (ATM) are not suitable for constrained urban airspace. This paper proposes and compares several traffic capacity balancing methods developed for a UTM system designed to be used in highly dense, very low-level urban airspace. Three types of location-based dynamic traffic capacity management techniques are tested: street-based, grid-based, and cluster-based. The proposed systems are tested by simulating traffic within mixed (constrained and open) urban airspace based on the city of Vienna at five different traffic densities. Results show that using local, area-based clustering for capacity balancing within a UTM system improves safety, efficiency, and capacity metrics, especially when simulated or historical traffic data are used. Full article
(This article belongs to the Special Issue Unmanned Traffic Management Systems)
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26 pages, 6026 KiB  
Article
An ETA-Based Tactical Conflict Resolution Method for Air Logistics Transportation
by Chenglong Li, Wenyong Gu, Yuan Zheng, Longyang Huang and Xuejun Zhang
Drones 2023, 7(5), 334; https://doi.org/10.3390/drones7050334 - 22 May 2023
Cited by 5 | Viewed by 1875
Abstract
Air logistics transportation has become one of the most promising markets for the civil drone industry. However, the large flow, high density, and complex environmental characteristics of urban scenes make tactical conflict resolution very challenging. Existing conflict resolution methods are limited by insufficient [...] Read more.
Air logistics transportation has become one of the most promising markets for the civil drone industry. However, the large flow, high density, and complex environmental characteristics of urban scenes make tactical conflict resolution very challenging. Existing conflict resolution methods are limited by insufficient collision avoidance success rates when considering non-cooperative targets and fail to take the temporal constraints of the pre-defined 4D trajectory into consideration. In this paper, a novel reinforcement learning-based tactical conflict resolution method for air logistics transportation is designed by reconstructing the state space following the risk sectors concept and through the use of a novel Estimated Time of Arrival (ETA)-based temporal reward setting. Our contributions allow a drone to integrate the temporal constraints of the 4D trajectory pre-defined in the strategic phase. As a consequence, the drone can successfully avoid non-cooperative targets while greatly reducing the occurrence of secondary conflicts, as demonstrated by the numerical simulation results. Full article
(This article belongs to the Special Issue Unmanned Traffic Management Systems)
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