# UAS in the Airspace: A Review on Integration, Simulation, Optimization, and
Open Challenges

Euclides C. P. Neto, Derick M. Baum, Jorge Rady de Almeida Jr., João B.
Camargo Jr., Paulo S. Cugnasca
Safety Analysis Group - School of Engineering (POLI)
University of São Paulo (USP)
São Paulo, Brazil
{euclidescpn, derick.baum, jorgerady, joaocamargo<EMAIL_ADDRESS>

###### Abstract

Air transportation is essential for society, and it is increasing gradually
due to its importance. To improve the airspace operation, new technologies are
under development, such as Unmanned Aircraft Systems (UAS). In fact, in the
past few years, there has been a growth in UAS numbers in segregated airspace.
However, there is an interest in integrating these aircraft into the National
Airspace System (NAS). The UAS is vital to different industries due to its
advantages brought to the airspace (e.g., efficiency). Conversely, the
relationship between UAS and Air Traffic Control (ATC) needs to be well-
defined due to the impacts on ATC capacity these aircraft may present.
Throughout the years, this impact may be lower than it is nowadays because the
current lack of familiarity in this relationship contributes to higher
workload levels. Thereupon, the primary goal of this research is to present a
comprehensive review of the advancements in the integration of UAS in the
National Airspace System (NAS) from different perspectives. We consider the
challenges regarding simulation, final approach, and optimization of problems
related to the interoperability of such systems in the airspace. Finally, we
identify several open challenges in the field based on the existing state-of-
the-art proposals.

_K_ eywords Unmanned Aircraft System (UAS) $\cdot$ Unmanned Aircraft Vehicle
(UAV) $\cdot$ Integration $\cdot$ Simulation $\cdot$ Optimization $\cdot$
Airspace $\cdot$ Evolutionary Computing $\cdot$ Air Traffic Control (ATC).

##  1 Introduction

Air transportation is essential for society, and it is increasing gradually
due to its importance [1] [2]. The growth in flights number makes the airspace
more complex while leading to higher revenue. There are several obstacles to
be overcome by authorities in the following years in terms of safety and
efficiency of airspace. The Air Traffic Control (ATC) is pivotal in optimizing
airspace, assuming that safety and efficiency are key aspects of airspace
operation [3] [4] [5]. The ATC is divided into ATC units, which are “generic
term meaning variously, area control center, approach control unit or
aerodrome control tower" [6]. These units are arranged to accommodate all
airspace users by creating sectors. The role of controlling aircraft in each
control sector is played by Air Traffic Controllers (ATCo), who communicate to
ATCos responsible for other sectors to provide smooth conduction of aircraft
throughout their flights.

The ATC targets offering suitable levels of safety and efficiency and
addressing complex situations. Moreover, ATC provides Air Traffic Services
(ATS) to flights through ATCo instructions. The primary objective of these
services includes avoiding mid-air collisions and collisions with obstructions
and optimizing and maintaining the flow of air traffic [7]. The ATCo conducts
the aircraft in a sector by applying techniques to improve safety and
efficiency (e.g., vectoring). These professionals act collaboratively from the
beginning to the end of each flight, and other ATCos are assigned to control
such flights once a new sector is reached. Conversely, an obstacle currently
faced is to maintain workload111Workload can be defined as a metric that
represents the difficulty of ATCo in understanding a particular situation [8]
and can be expressed in terms of seconds. level under an acceptable threshold.

Among the several safety threats in airspace operation, mid-air collision can
be highlighted, which depends on a set of events despite issues in aircraft
mechanical systems, such as high ATCo workload levels and loss of the
established minimum separation. There is an effort of authorities toward such
events (e.g., ATCo training for critical situations and design of safe
standard procedures). Furthermore, in cases of high air traffic density, a
safer measure of the capacity of a sector is based on ATCo workload [9] [10],
i.e., the number of aircraft that can be safely accommodated decreases when
there is a higher workload level. As ATCo workload levels are related to
safety and there is an understanding by research and operational community
that airspace complexity is one of the main factors that impact this metric
[11], situations that these professionals are not familiar with tends to be
more unsafe. Moreover, several variables compose complexity, such as traffic
density and mental factors [12].

To improve the airspace operation, new technologies are under development,
such as Unmanned Aircraft Systems (UAS) [13] and Decision Support Tools (DST)
for ATCos (e.g., Arrival and Departure managers) [14]. These new technologies
present advantages in many aspects, such as safety, efficiency, and airspace
capacity. Furthermore, the DSTs aim to lead ATCos to more effective decisions,
which tends to reduce the ATCos workload and, ultimately, to reduce airspace
complexity [11]. Although these technologies are used in different situations,
they may bring uncertainties since it is reasonable to consider that ATCos may
not be familiar with them. Furthermore, new technologies being integrated into
the airspace nowadays (e.g., UAS) may be typical in the future, increasing
this familiarity.

Moreover, the UAS is vital to different industries due to its advantages
brought to the airspace (e.g., efficiency) [15]. The UAS has been considered a
relevant topic in the engineering community due to its applications [16] and
consists of systems composed of subsystems such as Unmanned Aerial Vehicle
(UAV), its payloads, the control station, and communications subsystems [13]
[16]. Different types of UAS (e.g., Autonomous Aircraft - AA - and Remotely
Piloted Aircraft Systems - RPAS) present different subsystem requirements
(e.g., remote piloting interfaces are present in RPAS but not in manned
aircraft). For instance, the ground station used to pilot RPASs is not part of
the AAs, which are considered fully autonomous.

In the past few years, there has been a growth in UAS numbers [17] in
segregated airspace. However, there is an interest in integrating these
aircraft into the National Airspace System (NAS). These aircraft, which have
several military and civil applications, present challenges to their
integration to be faced by authorities in terms of safety, i.e., new ways of
reaching unsafe states are included in the airspace. For instance, bugs in
software may maneuver the aircraft and lead it to undesired headings. Also,
considering RPAS, failures in Command and Control (C2) link, i.e., the
connection pilots use to communicate to the aircraft, may lead to unsafe
states [18] [19].

The relationship between UAS and ATC needs to be well-defined due to the
impacts on ATC capacity these aircraft may present. Throughout the years, this
impact may be lower than it is nowadays because the present lack of
familiarity in the relationship between UAS and ATCo contributes to higher
workload levels. As UAS only operate in segregated airspaces, ATC tends to be
more concerned when controlling a gate-to-gate flight of these autonomous
systems. Different challenges to enable this integration must be addressed,
such as specific regulations, policies, and procedures, enabling technologies
and standards development for dealing with UAS [20]. As the integration of UAS
enables new applications and its use may increase in the future [21],
developing approaches to integrate it safely is essential.

Furthermore, the Terminal Control Area (TMA) is a critical control area
generally established at the confluence of Air Traffic Service (ATS) routes in
the vicinity of one or more major aerodromes [7]. In this area, the aircraft
tend to be closer to each other. In general, TMA is the most resource-
constrained component of the air transportation system due to the number of
aircraft that operate simultaneously [22]. Its complexity increases according
to the airspace configuration (e.g., traffic density and weather conditions).
Hence, operations in the TMA usually follow standard procedures established,
e.g., Standard Instrument Departure (SID) and Standard Instrument Arrival
(STAR).

However, standard procedures (e.g., STAR) cannot be followed in some cases,
e.g., in high traffic density. In these cases, a highly challenging ATCo task
is the sequencing of the aircraft during the approach, considering the arrival
segment and the final approach [23] [24] [25] due to complex maneuvers
constraints. To accomplish this, the aircraft are conducted in a way to avoid
conflict, i.e., in a way not to disregard the minimum separation requirements,
as well as to avoid flying through cumulonimbus (CB), which are cloud
formations that present a real impact on aviation [26]. Finally, the primary
objective of defining a final arrival segment is to deliver a set of aircraft
from the final sector of the TMA to the final phase of its landing procedure
(i.e., the final approach), taking operational efficiency and safety into
consideration.

Establishing final arrival segments for achieving optimized aircraft operation
in terms of safety and efficiency is not a simple task. From the safety
perspective, the ATCo workload related to conflict avoidance during this
phase, i.e., aircraft minimum separation from other aircraft and to the
cumulonimbus (CB), must remain at acceptable levels once an increase in this
metric may present impacts on safety levels. From an efficiency perspective,
the aircraft set must be delivered to the airport as soon as possible.
Depending on the scenario, the ATCo must act rapidly to avoid airspace
reaching unsafe states. As the number of aircraft increases, the situation
becomes more complex and, consequently, more difficult to be controlled by the
ATCo.

On the other hand, integrating UAS in the NAS airspace is a challenge
nowadays. According to ICAO [27], “the airspace will be organized and managed
in a manner that will accommodate all current and potential new uses of
airspace, for example, Unmanned Aerial Vehicles (UAV) and that any restriction
on the use of any particular volume of airspace will be considered
transitory". Furthermore, although rules for UAS flights are defined for
segregated airspace [19], the increasing interest in the usage of UAS for
different applications (military and civilian) leads to a need for integrating
them into the National Airspace System (NAS). To accomplish this, safety
levels must not be compromised [19].

Toward the challenges faced in the final sector in complex situations, the
presence of UAS is an important player. Due to lack of familiarity, it is
reasonable to consider that the ATCo may feel uncomfortable in controlling
autonomous aircraft, which is a result of the uncertainty on UAS operation
[28] [29]. However, the arrival procedure is a critical and complex task even
without the UAS presence, and definition sequencing solutions for both Manned
Aircraft (MA) and UAS, especially during the early stages of UAS integration
in the National Airspace System (NAS), may lead to higher ATCo workload
levels. Furthermore, there needs to be more simulation methods that include
the UAS in the final sector and include complex situations (e.g., bad weather
conditions).

Finally, measuring the familiarity of ATCo with a particular aircraft (e.g.,
UAS) is a challenge. Not only because UAS does not operate in the NAS
nowadays, but also because of the relationship between familiarity and
cognition. Measuring familiarity enables better sequencing solutions in
arrival procedures, especially from the ATCo workload perspective, i.e., from
the safety perspective.

Thereupon, the main goal of this research is to present a comprehensive review
of the advancements in the integration of Unmanned Aircraft Systems (UAS) in
the National Airspace System (NAS) from different perspectives. We consider
the challenges regarding simulation, the final approach, and optimization of
problems related to the interoperability of such systems in the airspace. We
also highlight several open challenges in the field based on the existing
state-of-the-art proposals. Finally, Figure 1 illustrates the main aspects
analyzed for UAS Integration, Simulation, and Optimization. For each area,
several aspects are taken into consideration based on their relevance in the
reviewed works. Finally, some aspects are included in more than one area.

Figure 1: Main aspects analyzed for UAS Integration, Simulation, and
Optimization.

This article is organized as follows: Section 2 reviews strategies focused on
the UAS Integration in the National Airspace System (NAS). Sections 3 and 4
analyze airspace simulation and arrival segment optimization efforts
considering the UAS presence. Finally, Sections 5 and 6 present the open
challenges and conclusions of this research, respectively.

##  2 UAS Integration in the National Airspace System (NAS)

This section presents works related to approaches of including and measuring
impacts of Unmanned Aircraft Systems (UAS) integration in the airspace from
different perspectives. The works presented in this section are classified
according to the presence of the critical aspects: Large Aircraft (LA),
Impacts on ATCo Workload (ATCoW), Levels of Familiarity (LF), and mixed
aircraft (MixA - UAS and Manned Aircraft operating together).

Shmelova et al. [30] present an approach based on statistical data to deal
with the problem of Unmanned Aerial Vehicles (UAV) flights considering
different tasks in emergencies, which are special situations and tend to
increase the Air Traffic Controller (ATCo) workload. Also, an analysis of the
emergency type is conducted and a sequence of actions is defined. The authors
present a motivation for the development of their research, which includes the
lack of algorithms to recommend actions for the UAV operator in an emergency,
problems in the decomposition of the decision-making process and the lack of
structure of Distributed Decision Support System (DDSS), which aims to
recommend actions to appropriate aircraft from a global perspective, for
remotely piloted aircraft. Furthermore, models are developed by the authors to
determine the optimal landing site in specific situations and search for
optimal flight routes. However, this effort only considers emergencies, and
the proposed model does not consider complex airspace, i.e., airspace with
many aircraft. Finally, impacts on ATCo workload due to UAS presence and lack
of familiarity of ATCo with this new technology are not taken into account.

Pastor et al. [31] aim to evaluate the interaction between a Remotely Piloted
Aircraft System (RPAS) and Air Traffic Management (ATM) considering that an
RPAS is being operated in shared airspace, i.e., along with traditional
aircraft in National Airspace System (NAS). This evaluation employs human-in-
the-loop real-time simulations that allow simulating activities from the RPAS
Pilot-in-Command (PiC) and the Air Traffic Controller (ATCo), and three
different perspectives: the separation management, the contingency management,
and the capacity impact in the overall ATM system. The experiments conducted,
which were realistic and without excessive complexity, presented
recommendations to improve the evaluation, e.g., preliminary analysis of
traffic to prevent separation conflicts and improvement of ADS-C flight intent
communication mechanism. However, this research does not consider complex
airspace scenarios regarding the number of aircraft.

The authors in [32] propose a geometrical horizontal detect and avoid
algorithm for UASs operation, based on ADS-B-equipped aircraft information, in
Terminal Control Areas (TMA), considering a constant speed. This approach
employed recorded commercial traffic trajectories and random conflict
scenarios with UASs. The main goal is to show the algorithm’s applicability in
ensuring the separation from traditional aviation, i.e., this research
considers a mix of manned and unmanned aircraft. Also, six different missions
are considered, such as flying straight or turning and climbing or descending.
Other important aspects observed were the influence of the various parameters
on the separation achieved and the number of maneuvers required, i.e., the
strategy used selects the best directions respecting the range of heading
degrees allowed. The experiments showed the proposal’s effectiveness, which
maintains the heading constant and changes it robustly if the minimum
separation threshold is greater than the current separation between the UAS
and a given aircraft. One should note that these methods were tested on 2850
realistic traffic scenarios, which were issued from data recorded in a French
Terminal Control Area (TMA). However, although there is a considerable effort
in the detection and avoidance process, the authors do not consider UAS a
large aircraft. Finally, the ATCo workload (e.g., the additional cognitive
workload related to UAS operation) is not evaluated.

The authors in [33] focus on possible guidelines for UAS integration in the
National Airspace System (NAS). The main objective of this approach is to
maintain the level of safety of UAS and traditional aircraft nearly the same,
which may lead the authorities to implement new airspace rules such as
additional separation for unmanned traffic. The authors also consider the
usage of Airborne Collision Avoidance System (ACAS) maneuvers and avoidance
logic. In this work, the authors conducted the experiments considering a
series of simulations that present a reduction in conflict potential (UAS and
traditional traffic). The reduction of impact on airspace operation,
considering the UAS integration, is also highlighted since its integration in
the NAS is a challenge in terms of future acceptance of these autonomous
systems. In this context, hazardous situations related to UAS operation are
stated, such as UA leaving cleared planned route, ATC having no position
information, and loss of communication. Furthermore, the reader should note
that UAS flights can be conducted with low interference considering proper
mission planning, although ATC needs to control these autonomous systems,
which tends to increase the workload of the ATCos. The authors also suggest
the presence of specialized UAS controllers, which could share the duty of
controlling the airspace among many ATCos. However, although the workload is a
concern of this paper, a workload evaluation is not conducted. Also, the
workload related to the operation of UAS does not include the additional
cognitive workload present, especially in the early stages of UAS integration.
Also, different levels of familiarity of ATCo with these systems are not
considered.

An approach for safety and risk assessment in unmanned aerial system (UAS)
operations, which employs stochastic fast-time simulation and is conducted in
the NAS, is presented in [34]. Considering that the integration of UAS in the
NAS is a concern to airspace regulators, the main goal of this research is to
calculate fatality risk and to measure how different aspects and phases of UAS
operations increase the risk. To accomplish this, the authors model and
simulate UAS operations over a specific hazardous region by applying different
stochastic parameters (e.g., altitude, performance variation, and latency).
Note that the risk analysis considers fatalities and is based on published
ground impact models, which enable the usage of fast-time simulation to assess
specific situations. Furthermore, the method adopted in this research, which
compared different risk analysis models, is important to highlight mitigation
actions for all stakeholders in the safety assessment. However, although this
paper discusses the importance of accurately measuring the risks of fatalities
in UAS operations, some aspects are not considered. For instance, the workload
associated with the presence of UAS in the airspace is not faced. Thus, the
level of familiarity of ATCo with this technology is not considered.

In [35], the effectiveness of geofencing systems (such as static and dynamic
design) for UAS, which defines geographical boundaries in specific airspace
areas, is analyzed. The authors also compare the geo-fencing effectiveness to
the current and traditional proposed regulations on collision avoidance
systems. To accomplish this, Monte Carlo simulations are employed, considering
growth and incident rates based on the incident data. In this context, plenty
of UAS (more than 1 million) are available to operate within the National
Airspace (NAS), but there is a need to integrate them safely into the NAS.
This process must be conducted to optimize the relationship between cost and
safety. Furthermore, UAS is considered disruptive technology to be included in
NAS, and operations cost reduction motivates such integration. Although even
considering the substantial growth of these aircraft in the past few years and
so forth, the step-wise increase of operational tests and global acceptance,
the number of incidents has also grown. This growth has been due to different
reasons, such as the disobedience of planned altitude and location by UAS. The
experiments showed that UAS operations conducted into regulated thresholds,
i.e., to specific geographical areas, provide a cost-effective method that
respects safety levels and eliminates 98% of the UAS incidents as reported by
FAA. However, this research does not consider aspects related to ATCo
operation, such as workload.

Gimenes et al. [36] propose guidelines to support UAS regulations for the
integration of fully autonomous UASs into the Global Air Traffic Management
(GATM) System and, consequently, into shared airspace. These guidelines are
proposed facing three perspectives: the aircraft itself, the Piloting
Autonomous System (PAS), and the integration of autonomous UASs in the
National Airspace System (NAS). Considering that there are social and economic
interests in UAS applications, enabling this technology to operate along with
Manned Aircraft (MA) has considerable potential. The main issue of this
integration is that aeronautical authorities should regulate UAS operations in
the NAS, although defining these rules is difficult since there is not a deep
understanding of UAS operations and how they behave in case of failures (e.g.,
contingency operations). Throughout the paper, the authors present the
guidelines with different focuses. For instance, regarding the “aircraft
focus", although it is not in the scope of this paper, the authors state that
it “should be submitted to at least the same processes and criteria of
developing, manufacturing and certification regarding avionic systems of
manned aircraft, aiming to reach the same safety levels". Furthermore, the
authors highlight that the UAS concept should be based on aeronautical
precepts and that the possibility of integrating UASs into airspace depends on
specific regulations. However, this research does not consider the ATCo
evaluation and the impact of UAS operation on ATCo performance.

In [37], the authors present a discussion on the integration of UAS in the
NAS. This problem is a complex system-of-systems problem, considering the
level of difficulty higher the technical challenges related to the
development, implementation, and validation of this technology. Considering
that the system design itself is a complex problem, the authors emphasize that
the operation of UAS into NAS depends on aviation regulatory authorities, but
this sort of regulation is not simple to be defined. The main challenge
identified is to design UASs with high safety standards that operate, such as
manned aircraft (e.g., transparency). UAS numbers have increased tremendously
in the last few years due to the distinct capabilities and cost advantages
compared to manned aircraft in most situations, enabling these aircraft to
operate alongside manned aircraft is desirable. Throughout this paper,
different regulations are presented, such as regulations followed in
Australia. Furthermore, this paper analyzes reasons for the difficulty in
integrating UAS in the NAS. However, although this research considers workload
an important aspect of UAS inclusion, it does not propose an approach to
evaluate it. Finally, the evolution in terms of the familiarity of the
relationship UAS-ATCo is not considered.

In [38], the authors aim to identify potential ways of mitigating issues
related to different UAS challenges. Also, a revision of some of the pros and
cons of these different approaches and recommendations for changes in
procedures, automation, and policy. The impacts of an integrated UAS operation
on ATC are not fully clear yet, even in less congested areas, but there is a
need to integrate this aircraft in terms of cost reduction and efficiency. The
MITRE Corporation, which is the corporation of the authors of this research,
has been using techniques to identify the impacts of UAS on ATC in the past
years, which has shown that, for instance, the process of filing flight plans,
the usage of latitude/longitude waypoints instead of named fixes or waypoints
and possible delays or loss of communication have considerable impact. More
specifically, the authors state that the impacts are presented in five major
areas: UAS flight planning and automation, the UAS control link (delays and
loss), UAS-specific information and procedures, ATC training, and UAS
interaction with the future NAS. However, although this research highlights
challenges of ATC in terms of UAS integration and considers ATCo workload as
an impacted metric, the level of familiarity of ATCo with a specific aircraft
is not considered, i.e., there is not a workload evaluation process that
highlights the difference between UASs of different familiarity (from the ATCo
perspective).

The authors in [39] deal with the problem of integrating UASs above urban
environments, i.e., into low-altitude airspace. This integration includes
major Terminal Manoeuvring Areas (TMA) and helicopter landing sites. A set of
data-driven modeling techniques are employed to assess existing air traffic as
starting for UAS operation. To accomplish this analysis, the authors exploit
low-altitude air traffic data sets in order to discover existing no-fly zones
and an alternative geometric approach to defining exclusion zones, which is
applied to a real region (Australia), including one International airport and
helicopter landing area. Considering that determining adequate exclusion zones
for unmanned aircraft in an urban environment is an important task and that
regulations may, in some cases, include UAS in these areas without
considerable reduction of risks of collision, the main goal of this research
is to propose an approach to define exclusion zone appropriately. The results
showed a need for more rigorous scientific approaches to safely integrate
these autonomous aircraft into shared and urban airspaces. However, although
this work constitutes an important and unique contribution to UAS integration
in the urban environment, aspects such as workload measurement during the
definition of these areas are not considered.

The authors in [40] propose a way to create a Risk-Informed Safety Case (RISC)
applied to the context of small UAS operation safety assurance. This approach
aims to facilitate safe and cost-effective operations of small UAS by
presenting the comprehensive measures considered to eliminate, reduce, or
control the safety risk. The RISC proposed comprises barrier models of safety,
which support the development of safety measures, and structured arguments,
which assure safety in operations (through, for instance, appropriate
evidence). The authors also propose a model for small UAS operational risk,
which considers, for instance, specific hazards (e.g., mid-air collision) and
operational risks which depend on the small UAS. Ultimately, this paper shows
key safety-related assurance concerns to be addressed and the development of a
layered framework for reasoning about those concerns, which can be useful for
regulators and various stakeholders in justifying confidence in operational
safety in the context of the absence of the relevant aviation regulations for
small UAS. However, although the authors focused on proposing an approach to
deal with the current state, i.e., a lack of presence of UAS in shared
airspace, this research does not measure the impact of these aircraft on ATCo
operation (e.g., workload) and, ultimately, into safety levels. Finally,
different levels of aircraft familiarity to the ATCo are not considered.

In [41], a new framework for system safety certification under conditions of
uncertainty is proposed considering a Bayesian approach to the modeling of the
average probability of failure conditions. Nowadays, the debates over
developing appropriate system safety requirements for UAS are heated. An
interesting point of view is approaching this analysis by determining the
allowable average probability per flight hour of failure conditions. Due to
the lack of knowledge and data to inform the assessment of failure
probabilities of UAS, a level of uncertainty may be considered during the
system safety assessment (SSA) process, which presents many advantages. The
conducted experiments showed the suitability of the proposed approach’s safety
measures. Thus, other sources of uncertainty are intended to be considered in
future works. Finally, the authors state that using a constant failure rate
model is challenged by using a Weibull distribution, which seems to be a more
appropriate representation of UAS failure occurrence. However, although there
is an effort to estimate UAS failures and an interesting approach that relates
uncertainty to safety assessment that can be applied to small and large UASs
is presented, this research does not focus on aspects related to ATCo
operation, such as communication to UAS.

Romero et al. [42] discuss on present and future of the Remotely Piloted
Aircraft System (RPAS) in terms of regulation by aeronautical authorities.
This discussion considers different countries (e.g., Colombia, Malta, and
Japan) aiming to understand the integration of RPAs in the NAS from the ATCo
perspective. An analysis of the existing classification types of RPAS (classes
one, two, and three) is conducted. Moreover, the results of integrating three
RPAS in the NAS, successfully performed in a real setting, from the air
traffic control center in Barranquilla (Colombia) are presented. Note that
there were no losses of separation with other aircraft or between RPAS and
that one of the authors of this paper, an air traffic controller of
Barranquilla, coordinated the different entities that participated in the
implementation of this successful operation of integrating RPA in the NAS.
Finally, a proposal is made to integrate this type of aircraft in the NAS,
which considers airspaces classification, RPA classification (in terms of
navigation performance), and contingency operation. However, although this
paper is an outstanding contribution due to the integration of RPAS into
shared airspace in a real setting, the authors do not consider future
scenarios in which RPAS may be represented by commercial aircraft. Finally,
different types of aircraft in terms of ATCo familiarity are not considered.

The basis to implement a risk model and general methodologies to investigate
RPAS safety, according to the operational scenarios defined by European
Aviation Safety Agency (EASA), is proposed in [43]. The authors analyzed
results achieved in experimental flights of multiple RPAS. As the modern
aeronautical scenario is being adapted to accommodate new key elements,
including the Remote Piloted Aircraft Systems (RPAS), initially used for
military purposes only, this new sort of aircraft is ready to become a new
airspace user in civilian applications and, even considering that it cannot
operate in the NAS nowadays, there is a potential growth expectation in terms
of investments on this technology. This research points out the hazards
related to RPAS operation in the NAS, such as failures in Command and Control
(C2) link, ATCo performance referred to with high workload situations, pilots’
performance with high workload situations, external factors (e.g.,
emergencies), and jamming. Moreover, the authors highlight that a requirement
for disclosing the airspace to RPAS is the implementation of a specific Safety
Management System (SMS) for every aeronautical operator. Finally, the
preliminary risk analysis presented in this research highlights many
possibilities to be further investigated in future works. However, although
this approach can be easily extended from small to large RPAS, this research
does not focus on the different maturity levels that each aircraft may present
in the relationship with the ATCo, which may have a considerable impact on
workload.

Perrottet [44] explores the challenges related to the application of
Performance Based Navigation (PBN) in UAS operation, which include GNSS
navigation, layered PBN procedures to UAS performance characteristics, and the
capability of performing instrument procedures (in case of failures in
communication link). The main goal of this integration is to enable UAS to fly
without limitations in airspace shared with other aircraft. However, the
primary focus of integrating these aircraft has been identifying a way to
compensate for the lack of a human pilot onboard, such as Detect and Avoid
(DAA) and Datalink technologies. The author also states that safety and
efficiency are two key metrics of airspace and that they may or may not be
inherently linked as in manned aviation, i.e., UASs may provide a more
independent relationship between safety and efficiency for specific
operations. Finally, this new balance between safety and efficiency must aim
to maintain the high level of safety observed in today’s NAS, which is a
requirement to turn the advantages provided by UAS reasonable. However,
although the authors deal with the problem of integrating UAS into shared
airspace, this research presents an overview of the challenges faced. One
should note that large aircraft are also considered, but aspects such as ATCo
workload are not considered.

In [45], the authors propose a qualitative approach for assessing the safety
of UAS operations when using Automatic Dependent Surveillance-Broadcast
(ADS-B) systems considering a new testing platform, which is called PIpE-SEC,
as a possible approach for this safety evaluation. This research focuses on
the influence of data integrity, which is considered a safety-related
parameter. The increase in UAS numbers is pressing authorities to design
airspace rules to integrate safely, although safety issues arise when both
manned and unmanned aircraft coexist in the airspace. Furthermore,
surveillance and data integrity play important roles in controlling these
aircraft. In this context, the positional information provided by the ADS-B,
which is essential to UASs control systems operation, interacts with the Sense
and Avoid Systems (S&AS) of the UAS to avoid exposure to unsafe situations.
Finally, the authors discuss the usage of a methodology previously applied to
manned systems for assessing safety and state that the adoption of the
presented methodology and tools enables the identification of appropriate
scenarios for the insertion of UAS along with manned aircraft, maintaining the
same safety. However, this research does not consider the impacts of
positional errors on aircraft with different maturity levels. For instance,
the impacts of positional error of UAS in the early stages of its integration
and in the long-term stage are not considered.

Oztekin et al. [46] propose a systems-level approach to analyze the safety
impact based on risk controls of introducing new technologies into the NAS,
such as UAS, considering Safety Management Systems (SMS) principles and
existing regulatory structure. Furthermore, the authors present a methodology
to identify minimum safety baselines for safe operations in the NAS and show
its applicability through a proof-of-concept study. In this context, UAS
emerges as a viable technology for potential civil and commercial applications
in the NAS, although it brings the need for a deeper analysis of safety
impact. A detailed outline of the concepts and methodologies used for
constructing a proof-of-concept study for the proposed approach, which
considers related hazards and underlying causal factors, is also presented.
Finally, the safety baseline proposed in this research identifies a set of
minimum risk controls for conducting safe operations. In future steps, the
authors intend to focus on identifying the UAS-specific components of the
developed safety baseline to identify hazards related specifically to the UAS
domain. However, although this research considers scenarios with both manned
and unmanned aircraft, aspects such as ATCo workload are not considered.

An architecture that provides data and software services to enable a set of
UAS platforms to operate in the NAS (including, for instance, terminal, en
route, and oceanic areas) is presented in [47]. The authors present the
general architecture and a Sense and Avoid (SAA) testbed implementation to
quantify the benefits. This architecture, based on a Service Oriented
Architecture (SOA) with open standards, aims to support UAS operations by
offering services to meet their specific requirements, such as command,
control, and data management. This proposed approach is considered guidance
and offers architectural best practices. Finally, even considering that an SOA
architecture makes some aspects of certification more challenging, this
approach presents some advantages and can be implemented to meet performance
requirements. One should note that certification may be more straightforward
considering the usage of formal service contracts, comprehensive interface and
quality of service specifications, and governance process in this SOA
architecture. However, this research does not provide specific services
considering each aircraft’s different maturity levels. Also, although this
contribution focuses on integrating UAS in the NAS, aspects such as impacts on
ATCo workload are not considered.

Wargo et al. [48] presents an integrated view of how enabling technologies can
support the Remote Pilot in Command (PIC) and the UAS operations in congested
terminal airspace operations. There is a desire, nowadays, to integrate large
and small UAS (e.g., RPAS) into the complex terminal environment and the
airport surface. The new surveillance technologies that are under development,
as well as the access to the NAS system information via System Wide
Information Management (SWIM), are manners for improving the remote UAS Pilot
in Command’s (PICs) performance and, consequently, to conduct UAS operations
safely in the terminal environment. Vendors can get data feeds for, for
instance, flight planning, airport status, and weather information through
these resources. All of these information streams provide better Situational
Awareness (SA) and a better understanding of the relationship of UAS to other
aircraft movements for remote pilots, which enables more efficient operations.
Furthermore, other enabling technologies are presented in this paper, such as
vision technologies, control techniques, and specific pilot alerts. Finally,
the authors have proposed an approach that would include additional
information to remote pilots’ flight control cockpit-like displays.

In [49], the authors present the advantages and disadvantages of four
architecture alternatives for enabling FAA Next-Gen National Voice System
(NVS), which are Legacy Analog, UAS Gateway Connected to Remote Radio Node
(RRN), UAS Gateway Connected to AVN and UAS Gateway over FAA Telecommunication
Infrastructure (FTI). Considering the architecture choice, UAS Gateway design
and functional requirements development are presented. As UAS technology
advances and operations become feasible and cost-effective, architectures that
support seamless interaction between UAS and the ATC are needed. These
architectures should include a UAS network Gateway for managing Air Traffic
Voice Node (AVN) within the airspace via a networked Ground-to-Ground (G/G)
connection. Several functional requirements must be considered in this
context, such as latency, security, access, communication, frequency, and
fault. On the other hand, the main components of the NVS include the ATC Voice
Node (AVN), which connects the pilot and ATC, and Local Radios (LRs), which
are used in tower operations. Finally, as current technologies adopted in UAS
operations introduce long latency and may sometimes be unavailable, enabling
UAS integration into the NextGen voice system is important. In conclusion, the
authors highlight that the 1-to-1 deployment of UAS Gateways to AVN and the
deployment of “access gateways” to provide a point of entry for the UAS PIC is
the recommended option. However, although this research is an important
contribution in terms of integration of UAS considering appropriate
communication, the relationship of these aircraft with the ATC is not
considered.

Finally, this section presented the works related to UAS integration in the
National Airspace System (NAS). Each research covers different aspects, and
Table 1 summarizes characteristics of all works based on the following
classifications:

  * •

Large Aircraft (LA): Indicates if the research considered large UAS in the
proposed approach;

  * •

Impacts on ATCo Workload (ATCoW): Indicates if the impacts related to UAS
operation on ATCo workload are considered;

  * •

Levels of Familiarity (LF): Indicates if the proposed integration approach
takes the familiarity of ATCo with the particular aircraft into account;

  * •

Mixed Aircraft (MixA): Indicates if UAS operations are considered along with
Manned Aircraft (MA) operations.

This table shows that most related works consider a mix of manned and unmanned
aircraft. Furthermore, many works consider UAS as a large aircraft. On the
other hand, although the impacts of UAS on ATC performance are important to be
measured and reduced, only two related works consider the integration from the
ATCo perspective. Also, none of the listed works treat all the criteria
presented in the Table.

Table 1: Review of UAS Integration in the National Airspace System (NAS). Related Work | LA | ATCoW | LF | MixA
---|---|---|---|---
[30] | X | X | X |
[31] | X |  |  | X
[32] | X | X | X |
[33] |  | X | X |
[34] |  | X | X |
[35] | X | X | X |
[36] |  | X | X |
[37] |  | X | X |
[38] |  | X | X |
[39] | X | X | X |
[40] | X | X | X |
[41] |  | X | X |
[42] | X | X | X |
[43] | X |  | X |
[44] |  | X | X |
[45] |  | X | X |
[46] |  | X | X |
[47] |  | X | X |
[48] | X | X | X |
[49] | X | X | X |

##  3 Simulation of UAS in the Airspace

This section presents works related to airspace simulation methods that may
include UAS. To identify research gaps, many aspects are analyzed. The works
presented in this section are selected as related works according to the
presence of the following aspects: the presence of UAS (UAS), Cognitive Impact
of Different Aircraft (CIDA), Bad Weather Conditions (BWC), Conflicts
Avoidance (CA), Air Traffic Controller (ATCo), and vectoring (Vc) and workload
(Wl) evaluation.

In [50], the authors present two simulation tools focused on unmanned aircraft
operations within shared airspace, considering the safety perspective. To
accomplish this, a fast pair-wise encounter generator is proposed to simulate
the See and Avoid environment, which is demonstrated through statistical
performance evaluation of an autonomous See and Avoid decision and control
strategy collected in experiments. Also, an unmanned aircraft mission
generator is presented, which helps to visualize the impact of multiple
unmanned operations. The authors intend to use these analysis tools in
exploring some of the fundamental and challenging problems faced by civilian
unmanned aircraft system integration and consider that these simple simulation
tools can be valuable when assessing a future aerospace environment. Finally,
future works, such as applying their strategy in random walk style missions,
are pointed out. However, this work does not include Air Traffic Controller
(ATCo) aspects in simulation, such as workload. Also, autonomous aircraft do
not present a relative cost due to the lack of familiarity with the airspace
operators (e.g., ATCo) present with this new technology.

Scala et al. [51] propose a methodology for developing an airport arrival and
departure manager tool. Optimization and simulation techniques are employed
for improving the robustness of the solution. The main goal is to help air
traffic controllers manage the inbound and outbound traffic without incurring
conflicts or delays, i.e., this tool can help them make the right decisions
quickly. The decisions taken in the present methodology for each aircraft are
related to entry time and entry speed in the airspace and pushback time at the
gate. Finally, this approach presents a smooth flow of aircraft both in the
airspace and on the ground. The experiments, which considered the Paris
Charles de Gaulle Airport as the case study, showed that conflicts were
sensibly reduced. However, although the number of conflicts is reduced in this
simulation tool considering this approach, Unmanned Aircraft Systems (UASs)
are not considered. Also, the uncertainty related to autonomous control
systems (such as the airport arrival and departure manager tool) is not
considered.

Farlik [52] proposes the concept of air force simulator operational
architecture. Considering that live military training in the airspace is
expensive and that information technologies have evolved in the past years,
simulation becomes a feasible alternative in training building military
simulation centers with a high level of realism may be useful in this sense.
To train a wide spectrum of personnel together (e.g., pilots and ATCos),
simulation capabilities are merged into a single robust simulation
environment, which considers, for instance, cooperation. Finally, although the
simulation of air defense operations with all its aspects is a complex
process, this paper stated the essential conceptual operational architecture
of the proposed air defense simulator, helping to structure future simulator
architecture according to military requirements. However, this paper presents
a set of recommendations but does not include the UAS presence, which may be
feasible in the future.

The authors in [53] present a simulation study on Air Traffic Control (ATC)
strategies aiming to use global traffic information to improve the decision-
making process in local ATC. Considering that ATC is a decentralized system,
the control sectors face the challenge of using all available information to
manage local air traffic safely, smoothly, and cost-effectively. The strategy
adopted means how to define and apply various local ATC rules (e.g., first-
come-first-served rule) to the coming-in traffic, i.e., traffic that will
enter the local sector and whose information is available in global ATC.
Finally, a simple ATC network model is set up, and a software simulation
system is proposed the simulation results showed that applying an
inappropriate set of rules can cause heavy traffic congestion, while an
appropriate strategy (based on global information) can significantly reduce
delays, improve safety, and increase the efficiency of using the airspace. The
authors also indicate future directions of this research, such as introducing
more ATC rules and studying the effect of each of them, collaborating with the
ATC industry to modify and improve the simulation systems, and designing
proper ATC strategies. However, although the proposed strategy considers the
ATC, UAS operation is not considered. Also, different costs, in terms of
workload, for different aircraft are not considered.

In [54], the authors present a framework for facilitating rapid development
and deployment of distributed simulations based on Java virtual machines
called Runtime for Airspace Concept Evaluation (RACE). Developing large,
distributed, human-in-the-loop airspace simulations maybe not be simple,
including sophisticated interactive visualization. This framework utilizes the
actor programming model and open-source components (e.g., Akka and WorldWind).
Finally, the authors highlight three main contributions, which are the
provision by actors of the basis for extensibility and scalability, the
seamless combination of functional programming (scala) and actors, and the
minimal core of RACE with the maturity of its third-party system basis allow
applications that go beyond simple simulations. However, although this
framework allows adaptations and extensions, it does not consider the UAS
presence and its interaction with Air Traffic Controllers (ATCo). Finally,
this research is not focused on the final sector of Terminal Manoeuvring Areas
(TMA).

AgentFly system is presented in [55] as fast-time simulation tool. The authors
state that this tool is appropriate for National Airspace System (NAS)-wide
experiments, and the cognitive behavior of Air Traffic Controllers (ATCo) is
considered. The United States NAS, for instance, is very complex, and lots of
information need to be shared from specific regions to the whole NAS. Also,
increases in air traffic lead to impacts on the difficulty faced by ATCo in
avoiding unsafe and inefficient operations. An alternative to the real
operation is real-time Human-In-The-Loop (HITL) simulation, which provides
valuable feedback on the behavior of human operators but presents limited
flexibility and high cost. Thus, AgentFly is proposed as a fast-simulation
tool and can be used to perform different experiments, varying the number of
aircraft, to facilitate the analyses of specific situations in airspace (e.g.,
conflict avoidance). Several simulations were conducted, and important metrics
were measured. The present results showed that this tool is appropriate to be
used as a tool for large-scale experiments, providing detailed data for
further analysis. However, although this paper considers the Remotely Piloted
Aircraft Systems (RPAS) integration as a possible implementation (including
many aspects of their operation as landing) and a human behavior model, which
considers workload, additional cognitive workload related to UAS is not taken
into account. Finally, the workload during arrival segment execution is not
computed using vectoring points and different costs in terms of workload if
considering different aircraft (e.g., traditional aircraft and UAS).

The authors in [56] aim to make ATM research results more comparable by
sharing tools and data using a fully open-source and open-data approach to air
traffic simulation. The main challenges were achieving high fidelity (e.g.,
aircraft performance) and increasing the community’s adoption by keeping the
program as simple as possible. Considering the adoption of this platform by
many users, this can be considered a useful tool in innovation and application
development (e.g., providing specific methods for different problems). The
paper describes the difficulties faced when using a fully open-data and open-
source policy in this area. However, this work does not consider the UAS
presence and its impacts on the ATCo workload.

Tra et al. [57] present conflict rate models to determine the intrinsic safety
of airspace designs, which consider conflicts between aircraft in different
flight phases. Fast-time simulations were performed for different layered
airspace concepts, considering unstructured airspaces. The results indicate
that the models can estimate the conflict rate for high traffic densities.
When comparing the different layered airspace concepts tested, the model
predicted, and the simulation results, a clear safety improvement can be noted
when the heading range is reduced. Thus the models can be used to study the
effect of airspace design parameters on the safety of airspace concepts.
However, although this research considers structured and unstructured
airspaces, the presence of UAS is not considered.

In [58], the authors introduce the Air Traffic Operations and Management
Simulator (ATOMS), an air traffic and airspace modeling and simulation system
for free-flight concepts. This tool simulates end-to-end airspace operations
and air navigation procedures for conventional air traffic. A multiagent-based
modeling paradigm for modular design and easy integration of various air
traffic subsystems is adopted. Also, advanced Air Traffic Management (ATM)
concepts that are envisioned in free flight are prototyped in this research,
including Airborne Separation Assurance (ASA), Cockpit Display of Traffic
Information (CDTI), and weather avoidance. The results showed that advanced
ATM concepts present an appropriate scenario for free flights. However, this
research does not consider the ATCo workload and the impact of the UAS
presence on it. Also, it does not consider a vectoring point-based workload
evaluation.

The authors in [59] focus on simulation-based Air Traffic Controller (ATCo)
training using the Beginning to End for Simulation and Training (BEST), which
is a simulation tool adopted in training organizations in Europe. Although the
BEST simulator covers all levels and types of training (e.g., basic,
validation and conversation refresher), this research is focused on the basic
part of the initial training. Furthermore, insights into the challenges the
candidates face when mastering the techniques of performance-based training
are presented. ATCos are responsible for guiding aircraft through the
airspace, and throughout the whole education and later work, their extensive
training (which considers practical exercises performed on computer devices)
is divided into three phases. They are the initial training (basic and rating
training), unit training (transitional, pre-on-the-job, and on-the-job
training), and continuation training (conversion and refresher training).
Moreover, BEST simulator meets all the objectives and requirements prescribed
for basic ATCo training. However, this research does not consider the aspects
related to UAS integration (e.g., increase in cognitive workload), and
different levels of aircraft in terms of ATCo familiarity are not considered.

Young et al. [60] proposes an approach to describe the process of creating and
using weather polygons for simulation and analysis activities using the
Federal Aviation Administration (FAA)’s Concept Analysis Branch, including an
example study focused on weather impacts on flights efficiency and tested in a
fast-time simulation environment. Considering that weather has substantial
impacts on the National Airspace System (NAS) and that most simulation and
analysis tools are unable to represent weather activity effectively, the
development of capabilities that benefits some operational improvements cannot
be quantified, i.e., the weather may impact negatively on solutions that
improve the airspace efficiency consider good weather conditions. The FAA’s
Concept Analysis Branch (ANG-C41) developed a tool to create weather polygons,
which is a concise model to store and process and can be modeled as restricted
airspace that moves across the NAS that considers high-fidelity weather data.
This enables the measurement of the impact of operational improvements on
weather-related flight delays, and, thus, an analysis of the efficiency of
current weather avoidance operations was conducted using weather polygons in
algorithms to calculate the distance of each flight from the weather at
different severity levels as well as to identify flights which rerouted to
avoid moderate to severe convective weather. Finally, capability enables the
FAA to represent the impact of convective weather on NextGen operational
improvements. However, although this research is broad in terms of weather
simulations, this does not include UAS operation in bad weather avoidance.
Furthermore, the ATCo workload related to this process is not considered.

McGovern et al. [61] propose and present an overview of a Monte Carlo-based
stochastic airspace simulation tool and its formulation as programming
languages, environments, and other development tools. The main objective is to
provide a documented, lifecycle-managed, multi-processor capable, stochastic
simulation capability to enable the analysis of procedures and equipment for
aircraft flight into shared airspace. Thus, the selection, design, and
implementation of the mathematical models and verification and validation
processes are conducted. Since real experiments are expensive and unfeasible,
modeling and simulation are often used to study the physical world.
Furthermore, navigation aids, surveillance systems, pilots, aircraft, Air
Traffic Controllers (ATCos), and weather are desirable features for a useful
simulation tool, and in this research, the authors consider all of them and a
Graphical User Interface (GUI) integrating world-wide photo-realistic airport
depictions and real-time three-dimensional animation. Finally, this paper
focuses on the interaction of components in shared airspace, and the software
tool and its formulation are presented. However, this work does not consider
the UAS integration into airspace and its impacts on ATCo operation as well as
the ATCo workload.

The authors in [62] developed a simulation component, considering the UAS
Traffic Management (UTM) paradigm, that supports near and long-term live
flight testing and exploration. The capabilities of the simulation tool are
depicted in this work. In this context, NASA has started to work
collaboratively in research with the Federal Aviation Administration (FAA) and
other stakeholders (government, industry, and academia) to explore the
concepts and requirements of safe and scalable operations of small Unmanned
Aircraft Systems (UAS) in low-altitude airspaces. Finally, a powerful research
and development platform capable of addressing the multitude of questions is
developed considering that the UTM laboratory is ideally suited to progress
the state of UTM research and knowledge. However, although this research
considers the UAS presence and its control, large aircraft (e.g., traditional
aircraft) are not considered. Also, the control of manned and unmanned
aircraft is performed by different agents, and the aircraft are not included
in shared high-altitude airspace.

Different utilization modes of Closely Spaced Parallel Runways (CSPRs), which
are employed in the construction of parallel runways, are analyzed in [63],
considering different thresholds. This analysis is conducted using the
simulation software SIMMOD, which was applied to build simulation models for
different utilization modes of runways with a staggered threshold. This
systematic analysis aimed to evaluate airport capacity and operational
efficiency quantitatively. The authors showed through experiments that,
considering the existing air traffic control operation rules, CSPRs usage
enables the airspace to support from 765 to 815 movements on each peak day.
Also, 55 movements are supported in each peak hour. If the runway threshold is
staggered in terms of the approach direction and a bypass taxiway is provided,
i.e., considering adaptations of the runway due to air traffic state (e.g.,
dynamic change to reduce arrival delay), the mode landing on the inside runway
and taking-off from the outside runway shows up as the most efficient mode,
which increases the operation efficiency by about 5%. However, this research
explores the capabilities of a widely used simulation tool to build simulation
models. Also, the focus is on evaluating landings and take-offs, which is
different from the final approach, although it substantially impacts this
previous phase. Finally, the presence of autonomous control systems and their
impacts on personnel performance are not considered.

In [64], a new 4D trajectory generation method is proposed. This method is
based on historical radar data processing, considering traffic flow theory to
generate the flight states and introducing the multiple interacting models
smoother and spline interpolation to determine the intermediate flying status.
4D trajectory generation, one of the most fundamental functions in the
airspace simulation system, is currently based on the partitioning of the
flight, i.e., the entire flight is divided into several parts, enabling the
usage of models to generate the flying states. However, in the method proposed
in this research, the problems of generating the initial state of an aircraft
and depicting the 4D flying status are addressed. The results presented in
this work, obtained from the simulated trajectories and real trajectories by
the MATLAB software, showed that the method is valid and practical. However,
the authors do not consider the interaction of these aircraft with the ATCo.
Also, problems related to workload aspects, as well as bad weather conditions,
are not faced. Finally, although the authors consider real data, the standard
curve rate employed in aviation is not highlighted.

Bucceroni et al. [65] propose a system for integrating Unmanned Aerial System
(UAS) visual observers into the distributed simulation capability of the
Federal Aviation Administration (FAA). This distributed simulation, which
employs large-scale virtual reality systems, is used to demonstrate terrain
surrounding flight tests in virtual environments and generate the observer’s
views (ground- and air-based). Three situations are considered: stationary
ground-based monitoring, mobile air-based monitoring, and seaborne monitoring.
As large-scale distributed visualizations are routinely used by organizations
(industry and government), this approach is beneficial and has considerable
importance in the research associated with the FAA’s current work on adapting
itself to Next Generation Air Transportation System, considering that this new
system will include UAS into shared airspace. Thus, locally caching real-world
terrain with data provided was integrated into a graphical interface to give
the operator the UAS position and information from multiple perspectives in a
distributed simulation. However, the simulation tool proposed in this research
does not include interaction between the aircraft operator and the Air Traffic
Controller (ATCo). Finally, this is a work conducted aiming at the future
integration of UAS into shared airspace, but it does not consider the impacts
these autonomous systems have on personnel performance (e.g., workload).

Borener et al. [66] present Unmanned Aircraft Systems (UAS) modeling and
simulation, which consider a use case scenario that is consistent with the
FAA’s concept of operations for integration of UAS into shared airspace and
employs sensing (using actual radar track traffic data) and medium fixed-wing
UAS. The proposed simulations offer functionality related to UAS operations,
such as ‘detect and avoid’, mission profiles, positional variance, performance
variance, fuzzy conflicts, variation in time spent in communication, and
deviation from planned or intent profiles. Based on the RAMS plus fast-time
simulator tool, the simulations aim to evaluate the separation indices and the
number and severity of the separation events. The experiments, conducted in a
simulated Houston Metroplex Environment, showed that multiple UAS would
considerably increase the likelihood of separation events and separation
critically indices and the usage of the "return to departure land site"
contingency operation in case of failures in UAS communication link has a
considerable impact on separation events. A difficulty faced by researchers,
though, is the lack of historical data on UAS operations. However, this
research does not include the interaction between UAS and ATCo; consequently,
the workload is not considered.

The authors in [67] describe the results of a Dynamic Density (DD) human-in-
the-loop simulation. The DD model adopted aims to measure the complexity of a
given airspace area, and measures presented at the US/Europe ATM 2003 Seminar
were used in this research. Thus, the simulation included Reduced Vertical
Separation on the Cleveland Air Route Traffic Control Centre’s airspace, and
the considered traffic was actively controlled throughout the simulation. Due
to the difficulty related to real-world simulation, i.e., it may not be
feasible to conduct experiments with real aircraft, simulations were adopted
in this research. One should note that human-in-the-loop simulations may offer
more accurate data on, for instance, airspace capacity evaluation. The
simulated experiments employed six Certified Professional Controllers (CPCs)
and one Operations Supervisor from Cleveland and were conducted in the high-
fidelity Display System Replacement (DSR) Laboratory. The experiments showed
the DD metric performed better than the aircraft count, which is a usual
complexity measure. However, although this research has valuable results in
simulation and airspace capacity and is considered specialists in Air Traffic
Control (ATC), UAS are not considered in the approach. Finally, cognitive
factors (e.g., lack of familiarity with ATCo with a specific aircraft) are not
considered.

In [68], the LMI ACAS (Airspace Conflict Analysis Simulation) tool is
presented. This 3-dimensional simulation modeling tool, and its application
meets the analytical requirements. The benefits of implementing a
multidimensional visualization are also presented. The conducted case study,
which employs the ACAS tool for a safety risk assessment based on conflict
probability, demonstrates the capacity of the framework to evaluate safety
risk. Also, a set of concerns that include traffic growth, Next Generation Air
Transportation System (NextGen) technologies, dynamic airspace sector
reconfiguration, and the integration of UAS into shared airspace are
considered. As NextGen is under development, modeling complex, diverse future
scenarios is important, thus, better solutions can be provided in terms of
specific metrics (e.g., safety). Some causes and consequences related to risk
analysis are pointed out. For instance, an increase in passenger demand leads
to a higher traffic density. Considering that ACAS is not meant to be a NAS-
wide simulation of all aspects of flight, this research proposes an agile tool
for exploring NextGen aviation concepts and technologies from the safety
perspective. However, this research does not include the cognitive workload
associated with special aircraft operations (e.g., UAS).

In [69], numerical simulations are used in order to demonstrate the
effectiveness of the proposed conflict management approach, which ensures
conflict avoidance among aircraft and the transition of aircraft into adjacent
airspace. To avoid conflicts, complexity is modeled as aircraft heading and
speed deviations in a given sector. Considering more than one sector, a
specific architecture is proposed for planning to minimize complexity for the
neighboring sectors. More specifically, the conflict avoidance problem can be
seen as a mixed integer Linear Programming (LP) subject to maneuver
constraints. Thus, the aircraft can find the optimal solution by solving the
LP problem, resolving conflicts among the aircraft, and reducing the air
traffic complexity of the neighboring sectors. Moreover, the proposed conflict
management algorithm can identify aircraft’s optimal conflict resolution
maneuver in near real-time considering multi-sector environments. The authors
intend to investigate the relationship between maneuver constraints and
traffic complexity in future works. However, although this research is
interesting from the conflict avoidance perspective, it does not deal with
fully autonomous or remotely piloted aircraft. Finally, ATCo operation and the
impacts of the proposed approach on his/her workload are not considered.

In this section, the works related to airspace simulation were presented. Each
work covers different aspects, but to identify the similarities and
differences, Table 2 presents all works, which are classified as follows:

  * •

Unmanned Aircraft System (UAS): Indicates if UAS are considered in a given
research. One should note that Remotely Piloted Aircraft Systems (RPAS), as a
sort of UAS, are also considered.

  * •

Cognitive Impact of Different Aircraft (CIDA): Indicates if the impacts
related to special aircraft (e.g., UAS) operation on personnel performance.

  * •

Bad Weather Conditions (BWC): Indicates if the proposed simulation tool deals
with the challenges imposed by bad weather conditions.

  * •

Conflicts Avoidance (CA): Indicates if conflict avoidance is prioritized in
the given simulation tool.

  * •

Air Traffic Controller (ATCo): Indicates if ATCo operation is one simulation
focus.

  * •

Vectoring (Vc): Indicates if aircraft are controlled by Vectoring Points (VP).

  * •

Workload (Wl): Indicates if the workload of ATCo is evaluated in the
simulation tool.

This table shows that most related works deal with collision avoidance
problems in simulation, and many consider the ATCo operation. Few works
include weather conditions, vectoring, and workload evaluation in experiments.
Thus, UAS appears in a few works. Finally, none of the listed related works
treat the cognitive impact on, for example, workload due to the lack of
familiarity of ATCo with a new aircraft (e.g., UAS). Also, none of the listed
works treats all the criteria presented in the Table.

Table 2: Review of UAS simulation in the National Airspace System (NAS). Related Work | UAS | CIDA | BWC | CA | ATCo | Vc | Wl
---|---|---|---|---|---|---|---
[50] |  | X | X |  | X | X | X
[51] | X | X | X |  |  | X | X
[52] | X | X | X |  |  | X |
[53] | X | X | X |  |  |  |
[54] | X | X |  |  |  | X | X
[55] | X | X | X | X |  | X |
[56] | X | X |  |  |  |  | X
[57] | X | X | X |  | X |  | X
[58] | X | X |  |  | X | X | X
[59] | X | X | X |  |  |  | X
[60] | X | X |  |  | X | X | X
[61] | X | X |  | X |  | X | X
[62] |  | X |  |  |  |  | X
[63] | X | X | X |  |  | X | X
[64] |  | X | X |  | X | X | X
[65] |  | X | X | X | X | X | X
[66] |  | X |  |  | X | X | X
[67] | X | X |  |  |  |  |
[68] |  | X |  |  |  | X |
[69] | X | X | X |  |  |  | X

##  4 Arrival segment optimization considering UAS

This section shows a literature review conducted toward optimization
approaches for arrival segment design considering the UAS presence. The
proposals are analyzed from different perspectives. To select and classify the
related works present in this section, the consideration of following aspects
of each approach are taken into account: National Airspace System (NAS), Final
Arrival Segment Design (FASD), Complex Situations (CS), Bad Weather Conditions
(BWC), Minimum Separation (MS), UAS presence (UAS), and Time as a Constraint
(TC).

Alonso-Ayuso et al. [70] presents an approach that employs a mixed integer
linear approximation to a Mixed Integer Nonlinear Optimization (MINO) model
for the conflict resolution problem in air traffic management, i.e., for
providing aircraft configurations to avoid conflicts, which is the loss of the
minimum separation between two given aircraft. The problem is solved by
considering an initial position of a set of aircraft and applying changes to
their position, velocity, and heading angles. Thus, a multi-criteria scheme
and a Sequential Mixed Integer Linear Optimization (SMILO) approach are also
presented. This is due to the achievement of solutions in a short computing
time. Furthermore, a comparison between the results obtained by using the
state-of-the-art MINO solvers and SMILO performance in a broad testbed is also
considered, which showed that both presented similar solutions, but the
proposed approach requires a very short computing time. Finally, the authors
highlight that for large-size instances (e.g., above five aircraft), the
computing time is higher than the one required by real-life operational
applications and that other meta-heuristics can reduce even the computing time
without deteriorating the SMILO solution as a future research line. However,
this research does not consider the operation of UAS in the NAS.

The authors in [71] present a cooperative multi-aircraft Conflict Resolution
(CR) method based on co-evolution. The paths are composed of sub-populations
considered in a Particle Swarm Optimization (PSO) implementation, in which the
fitness is evaluated by cooperation among individuals from different sub-
population and is adopted for its advantages such as fewer parameters and
computation and faster convergence. One should note that each particle is seen
as a point of D-dimension space. Further, an encoding method with an adaptive
searching mechanism is introduced to improve the searching efficiency.
Compared with Genetic Algorithms (GA) currently being used for conflict
resolution path optimization, the results achieved by this approach achieved
higher system efficiency, which is a manner to measure how similar a given
path is to the smallest possible path. Considering 2, 4, and 6 aircraft, the
proposed approach outperformed the GA approach. However, although this
research employs the PSO successfully, this research does not consider the
fitness evaluation and particle update processes to be conducted in parallel
for each particle, which can improve the performance considerably. Also, bad
weather conditions are not taken into account. Finally, the UAS presence and
arrival segments definition are not considered.

Ahmed et al. [24] present an evolutionary method for optimizing the aircraft
path planning algorithm in Terminal Maneuvering Area (TMA). This method, which
provides near-optimal aircraft arrival sequences, aims to deliver the aircraft
to the Final Approach Fix (FAF). The paths are built to conduct the aircraft
from the Initial Approach Fix (IAF) to the FAF considering intermediates
waypoints called Intermediate Fix (IF). The classic Genetic Algorithm
(GA)-based optimization technique with conflict detection and resolution used
in this effort minimizes the inter-arrival time. Furthermore, conflict-free
path planning for an Air Traffic Controller (ATC) is also obtained. One should
note that conflict between any two aircraft is detected based on their future
arrival time at the waypoint. The results show that the proposed approach
provides a near-optimal solution compared to the traditional GA-based
algorithm, which does not consider airspace constraints (e.g., speed).

In [72], the authors proposed a mixed integer linear programming formulation
to optimize, in real-time, the take-off and landing operations at a busy
Terminal Maneuvering Area (TMA) in case of traffic congestion by investigating
the trade-off aspects between performance indicators of practical interest.
This method also considers safety constraints with high precision. As TMAs are
becoming problematic, especially in the major European airports, since there
is a limited possibility of building new infrastructures, alternative
solutions (e.g., optimization models) are desired. The real-time problem of
effectively managing aircraft operations is challenging, especially due to the
inclusion of safety regulations into the optimization model and several
performance indicators. This inclusion leads to achieving feasible and
reasonable solutions in terms of safety and efficiency, even considering that
there is no well-recognized objective function and traffic controllers often
use simple scheduling rules. The experiments were performed considering
simulated scenarios in the two major Italian airports, Milano Malpensa and
Roma Fiumicino. In this context, random landing and take-off aircraft
disturbances are built. In the optimization process, practical-size instances
are solved to (near) optimality by employing a commercial solver. Finally, a
computational analysis enables the selection of solutions that presents
considerable quality in balancing the various indicators trade-off. However,
this research focuses on scheduling and does not consider the presence of UAS
and the impact of the inclusion of this new technology into the ATC.

Samà et al. [73] deals with the TMA aircraft scheduling problem, which
requires conflict-free schedules for all aircraft, whereas the overall
aircraft delays are minimized. Furthermore, this research also deals with the
aircraft landing trajectory optimization problem, which requires a landing
trajectory that minimizes the travel time or the fuel consumption for each
aircraft. In this context, a framework for the lexicographic optimization of
both problems is proposed, which solves the two problems sequentially based on
a defined lexicographic order of importance for the performance indicators,
i.e., the most important performance indicator defines the first problem to be
optimized. Note that the second problem is solved considering some outputs of
the solution of the first problem. The experiments, performed on simulated
Milano Malpensa airport instances and considering different optimization
lexicographic orders and performance indicators, show the existence of
performance gaps between the optimized indicators of the two problems,
highlighting the multi-objective nature of the problem when different
lexicographic optimization approaches are considered. However, this research
does not consider some aspects, such as bad weather conditions.

A number of algorithmic improvements implemented in the AGLIBRARY solver, a
state-of-the-art optimization solver for complex routing and scheduling
problems, to improve the possibility of finding good quality solutions
quickly, is presented in [74]. Intelligent decision support systems for the
real-time management of landing and take-off operations, which can be
effective in helping ATCos at busy Terminal Control Areas (TMAs), aim to
optimize aircraft sequencing. This problem, which can be faced as a mixed
integer linear program, is strongly NP-hard, and heuristic algorithms are
typically adopted in practice to compute good quality solutions in a short
computation time. In this context, the framework proposed in this paper starts
from a feasible initial solution for the aircraft scheduling problem with
fixed routes, computed via a truncated branch-and-bound algorithm, and,
further, metaheuristics (e.g., variable neighborhood search, tabu search, and
hybrid schemes) are applied to improve the solution by re-routing some
aircraft in the TMA. Finally, the results showed that the metaheuristics
quickly achieve solutions of remarkable quality compared with a commercial
solver. However, parallel implementations of the metaheuristics, which may
reduce the execution time considerably, are not considered. Finally, UAS
presence is not taken into account.

The authors in [75] apply heuristic and genetic algorithms approach for the
path planning problem for UAVs. This approaches consider emergency landing
procedures and aim to mitigate the probability of reaching unsafe situations.
The path re-planning, caused by several factors such as equipment failures and
leads missions to be aborted by executing an emergency planned landing, is
introduced through a mathematical formulation. In this context, path planning
approaches that employ greedy heuristic, which aims to find feasible paths
quickly, genetic algorithm, and multi-population genetic algorithm, which
tends to return better quality solutions, are introduced and evaluated
considering a large set (600 maps) of scenarios. The experiments conducted
using the FlightGear simulator showed that all methods could land the aircraft
appropriately for about 67% of scenarios considered. The type of landing
executed by the UAV was evaluated under two situations. First, The UAV landing
is evaluated, taking into account the chance to save the UAV without putting a
risk on people, properties, or itself. Next, the UAV landing is evaluated,
considering the emphasis on saving people and properties without caring about
UAV damages. Finally, statistical analysis reveals that combining the greedy
heuristic with the genetic algorithms is suitable for this problem. Although
this paper deals with path planning, it is not focused on the National
Airspace System (NAS). Furthermore, the presence of the ATC is not taken into
account.

A framework and a formulation for solving path planning problems for multiple
heterogeneous UAVs with uncertain service times for each vehicle–target pair
is presented by Sundar et al. [76]. The vehicles, which differ in their motion
constraints and are located at distinct positions at the beginning of the
mission, consider a penalty related to the duration of their total service
time. The main goal is to find a tour of each vehicle that starts and ends at
its respective position. This considers that every target is visited and
serviced by some vehicle, and the sum of the total travel distance and the
penalty applied to all vehicles are minimized. Furthermore, the authors
present the theoretical properties and advantages of using a two-stage
stochastic problem formulation to solve this problem instead of using a
deterministic expected value formulation. Finally, extensive numerical
simulations that compared these two formulations also corroborate the
effectiveness of the proposed approach. However, although this research can be
adapted to be applied to problems related to the NAS (e.g., aircraft
rerouting), this is aimed to be applied to segregated airspace missions.
Furthermore, aspects such as bad conditions are not taken into account.
Finally, arrival segment constraints are not taken into account.

In [77], a fast algorithm that finds collision-free 3D paths for small UAS in
urban environments is introduced and combined with an algorithm that computes
approximate Euclidean shortest paths. This algorithm reduces the number of
obstacles present in the pathfinding process,, considering that the studied
environments are expressed as three-dimensional scenarios and the objects as
vertical polyhedra. The reader should note that this approach aims to reduce
the computation time in a more practical situation, i.e., the algorithm
proposed is inefficient in complex situations. Thus, there are situations
where the algorithm does not perform well, for instance, scenarios that
include tall objects. Experimental cases showed that this approach is
competitive in terms of speed and solution quality compared to solutions
present in the literature for more realistic scenarios. Furthermore, the
authors intend to extend this method for more complex scenarios in future
works. However, this research does not consider the application in the
National Airspace System (NAS). Also, evolutionary approaches (e.g., Particle
Swarm Optimization) are not explored as an alternative to solving complex
situations. Finally, some important factors impacting aviation for segregated
airspace and NAS, such as bad weather conditions, are not considered.

In [78], an approach that employs a single UAV for providing wireless coverage
for indoor users inside a high-rise building under disaster situations (e.g.,
earthquakes), considering the failures in cellular networks, is proposed. To
accomplish this, the authors assume that the locations of indoor users are
uniformly distributed on each floor. Furthermore, a Particle Swarm
Optimization (PSO) algorithm is used to find an efficient 3D placement of a
UAV that minimizes the total transmit power required for the coverage. The
experiments, which considered 50 population size, 50 maximum iteration number,
and 20 users on each floor, showed that the proposed approach minimizes the
total transmit power required to cover the indoor users considering a uniform
distribution of users per floor. Note that the authors state that the PSO is
chosen due to the problem’s intractability, given its characteristics. In
conclusion, this research adopts a traditional implementation of the PSO
algorithm and adapts it to the problem. However, changes that may improve the
time spent finding an appropriate solution in terms of feasibility and
fitness, e.g., polarization, are not considered. On the other hand, this
effort focus on small UAS, i.e., the NAS is not considered. Finally, complex
situations and bad weather conditions are not included in this approach.

Jiang et al. [79] establish the model of task assignment for UAV in logistics
regarding the Vehicle Routing Problems with Time Windows (VRPTW). In the past
few years, there has been a growth in research achievements in logistics and
UAV separately, whereas the research achievement on the combination of these
areas steadies stable. Effective logistics systems and task assignments reduce
operating costs and improve transport efficiency. In this context, the model
proposed in this research considers different constraints, such as weight
coefficients, time-windows constraints, and the constraints of the UAV.
Furthermore, the Particle Swarm Optimization (PSO) algorithm is used for
solving the task assignment problem due to its suitability for dealing with
complex combinatorial optimization problems. Note that the PSO implementation
presents some modifications since the original PSO algorithm is only suitable
for the continuous space optimization problem. In this paper, the task
assignment for UAV is an integer linear programming problem. The conducted
experiments showed that the PSO is efficient in solving the problem of task
assignment for UAV, and, in comparison with a traditional Genetic Algorithm
(GA), this approach presented a higher success rate and a lower average
running time.

A new optimization problem for solving conflicts is presented by Hong et al.
[80]. This method allows aircraft to change their heading angle and speed to
optimize their trajectory. The performance index is expressed in terms of the
variation of the aircraft arrival time caused by conflict resolution
maneuvers, i.e., higher performance indices are computed in situations where
this time variation is low. In order to accomplish conflict resolution and
proper flow management, metering constraints (e.g., aircraft arrival time) are
introduced together with separation constraints. In this context, the optimal
solution is obtained by utilizing Particle Swarm Optimization (PSO), and
numerical, and Monte Carlo simulations are conducted to evaluate the
performance of the proposed algorithm. Due to the considerable ease of PSO in
solving complex nonlinear problems, several performance indices and
constraints are considered without the limitations of linear approximation or
a complex procedure, which may involve a certain level of imprecision. The
simulation results showed a significant reduction in the variation of the
aircraft arrival time and the magnitude of the maneuvers, i.e., heading angle
and speed changes.

Marinakis et al. [81] deal with the Constrained Shortest Path problem, which
is a well-known NP-hard problem, by proposing a new hybridized version of
Particle Swarm Optimization (PSO) algorithm, which is a population-based swarm
intelligence method, with Variable Neighborhood Search (VNS), which is an
algorithm applied to optimize the particles’ position. Although in the
proposed algorithm, a different equation for the velocities update of
particles is considered, and a new neighborhood topology is employed, an issue
of applying the VNS is the identification of the suitable local search method
for a given problem. In this sense, a number of continuous local search
algorithms are used and tested in a number of modified instances from and
further comparisons with classic versions of PSO. Finally, the experiments
showed that the proposed algorithm has satisfactory efficiency and results. In
future directions, the authors highlight the application of this methodology
to more difficult problems.

In [82], the authors present an optimization algorithm for solving the problem
of arrival aircraft trajectory, which aims to find the best solutions for
vertical flight profile considering the Required Time of Arrival (RTA) and
constraints of Terminal Maneuvering Area (TMA) and aircraft performance.
Firstly, the Base of Aircraft Data (BADA), which is an open-source database of
aircraft performance aspects and is used in simulation tools, is used to
identify the aircraft’s aerodynamic and fuel consumption. Then, a method for
optimizing the trajectories is proposed based on an Improved Particle Swarm
Optimization (IPSO), in which particles’ inertia decreases from 1 to about 0.5
as long as they get closer to near-optimal solutions, with Successive
Quadratic Programming (SQP). During the optimization process, the IPSO is
employed in finding a near-optimal solution, and then, the SQP is used to
enable a quicker finding of an accurate solution. Furthermore, this approach
is compared to standard PSO, which shows that its performance is effective for
trajectory optimization problems. However, this proposal is not focused on
aspects of UAS integration during the optimization process. Finally, although
fixed weights are employed in our proposal, the parallel architecture
considered tends to improve the algorithm efficiency.

Girish [83] proposes a Hybrid Particle Swarm Optimization-local search (HPSO-
LS) algorithm, in a rolling horizon framework, for dealing with the Aircraft
Landing Problem (ALP), which consists of the allocation of arriving aircraft
to runways and the assignment of a landing time to each aircraft. The main
goal of this research is to minimize the penalty costs due to delays in
landing times. Note that the rolling horizon framework is used as an online
optimization strategy considering a fixed time horizon. The presented results
showed that the proposed algorithm effectively solves the problem and compares
with existing approaches from the literature (e.g., PSO variants) in scenarios
involving up to 500 aircraft and five runways, the RH-HPSO-LS showed to be a
more appropriate technique for this problem. In future works, the author
intends to improve approaches to reduce the computational time requirements
for enabling real-world applications, i.e., real-time applications. However,
this effort does not cover the final arrival segments design from the final
sector. Also, although the techniques employed are interesting for finding
good solutions, the optimization is not built considering aspects of UAS
integration in the NAS.

Ribeiro et al. [84] propose a framework that integrates performance
preferences of landing aircraft in Continuous Descent Arrival (CDA) operations
that deals with managing building flight trajectories, which are optimized
reduce fuel burn and emissions during the descent/approach phase. This
approach is special interesting once the maximization of airspace efficiency
and capacity, which needs to be addressed considering local airspace
requirements and constraints, is related to the optimization of air traffic
trajectories. The authors highlight that the Air Traffic Control (ATC) agent,
responsible for conducting the air traffic to specific trajectories, employs a
Particle Swarm Optimization (PSO) algorithm to build feasible and safe
solutions for arrival sequencing. The results showed that considering the data
from Brasilia International Airport (SBBR), the proposed approach enabled 77%
of the air traffic to accomplish their desired time window flying. Finally, as
future intentions, the authors aim to deal with en-route trajectory conflicts
and capacity constraints. However, this research does not consider some
aspects, such as bad weather conditions.

The authors in [85] focus on the aircraft landing optimization problem
considering both the landing routes and the landing order of aircraft. The
main goal is to minimize the occupancy time of the airport, which leads to an
increase in airspace efficiency. This approach considers dynamic weather
conditions and other aircraft’ landing routes changes. To deal with this
problem, the hierarchical evolutionary computation is proposed, which
generates candidates for the main landing route of all aircraft. Furthermore,
a good combination of landing routes for all aircraft is considered to
minimize the occupancy time of the airport. The experiments showed that the
proposed strategy generates robust and orderly landing routes. However, the
results have only been obtained from one simple grid map, which simulates the
flying area of the aircraft and further careful qualifications and
justifications (e.g., other maps or a different number of aircraft) represent
the future intentions of the authors. Furthermore, our proposal considers
complex situations in which feasible solutions in terms of efficiency and,
especially, in terms of safety are required. Finally, the UAS integration,
which may be an important airspace user in the next years, is considered.

Narayan et al. [86] proposes a novel approach for optimizing 3D flight
trajectories considering real-time planning deadlines for small UAS operating
in challenging environments, i.e., environments with obstacles. In this
approach, which generates feasible solutions, sets of candidate smooth flight
trajectories are generated, and, considering that in typical small UAS
operations, multiple objectives may exist, a multi-objective optimization is
employed since it may allow the discovery of solutions that better reflects
overall mission requirements. Note that, in this context, real-time planning
constraints may be imposed during the optimization process to avoid obstacles
in the immediate path, and this approach considers a novel Computationally
Adaptive Trajectory Decision (CATD) optimization system to manage, calculate
and schedule parameters associated with trajectories building to ensure that
the feasible solutions are offered taking processing duration as a constraint.
In conclusion, the authors point out that this approach may potentially be a
more efficient use of the computational time available. However, this research
is intended to be applied to segregated airspaces. Furthermore, weather
conditions are not taken into account.

The authors in [87] propose an online method based on the Estimation of
Distribution Algorithm (EDA), which has become a hot topic in the field of
evolutionary computing, for the real-time Aircraft Arrival Sequencing and
Scheduling optimization problem. This problem, considered a hot topic in Air
Traffic Control (ATC) contributions, has been proven to be an NP-hard problem.
Although many efforts have been made by modeling this problem in a static
case, the air traffic environment in the airport is dynamic and constantly
changing. Since new aircraft are arriving at the airport continually, the
corresponding adjustments should be considered for the scheduling definition.
In this context, the method focuses on aircraft that have already arrived at
the TMA but have not been assigned to land. The experiments highlighted that
the method effectively achieves appropriate solutions for the Aircraft Arrival
Sequencing and Scheduling optimization problem. However, this contribution
does not include the operation of UAS in the NAS and all challenges it brings
to the sequencing problem. Furthermore, bad weather conditions are not taken
into account. Finally, the fitness evaluation does not consider the impacts of
a given sequencing solution on the ATC.

Bennell et al. [88] deal with scheduling aircraft landings on a single runway.
The time window constraints for each aircraft’s landing and the minimum
separation between consecutive aircraft and, consequently, consecutive
landings are two important metrics for the sequencing problem. Note that the
separation between aircraft depends on specific factors, such as the weight
classes. Thus, a multi-objective formulation that considers both the runway
metrics (throughput, earliness, and lateness) and the fuel cost related to
aircraft maneuvers and additional flight time is employed to achieve the
landing schedule. This proposal also considers the static/off-line problem, in
which details of the arriving flights are provided in advance, and the
dynamic/online problem, in which the flight arrival information becomes
available over time. The experiments showed that efficient runway throughput
results were achieved for both static and dynamic problems, considering the
employment of different meta-heuristics.

In this section, the works related to air traffic sequencing optimization were
presented. Different aspects are covered by each work, but in order to
identify the similarities and differences, Table 3 presents all works, which
are classified as follows:

  * •

National Airspace System (NAS): Indicates if the optimization method is
intended to be applied in situations of NAS;

  * •

Final Arrival Segment Definition (FASD): Indicates if the proposed method is
focused on the final arrival segments design;

  * •

Complex Situations (CS): Indicates if the optimization method is developed
considering the sequencing of many aircraft, which constitute a complex
situation;

  * •

Bad Weather Conditions (BWC): Indicates if the proposed solution takes bad
weather conditions into account;

  * •

Minimum Separation (MS): Indicates if the proposed method applies minimum
separations for each aircraft in order to maintain the safety levels;

  * •

UAS Presence (UAS): Indicate if the proposed solution considers the presence
of the UAS and its impacts on sequencing;

  * •

Time as a Constraint (TC): Indicate if processing duration is analyzed in the
approach, i.e., if the problem faced is a real-time problem.

Table 3: Review of UAS traffic sequencing optimization in the National Airspace System (NAS). Related Work | NAS | FASD | CS | BWC | MS | UAS | TC
---|---|---|---|---|---|---|---
[70] |  | X |  | X |  | X |
[71] |  | X |  | X |  | X | X
[24] |  |  |  | X |  | X | X
[72] |  | X |  | X |  | X |
[73] |  |  |  | X |  | X | X
[74] |  |  |  | X |  | X |
[75] | X | X | X |  | X |  |
[76] | X | X | X | X |  |  |
[77] | X | X | X | X | X |  |
[78] | X | X | X | X | X |  | X
[79] | X | X |  | X | X |  |
[80] |  |  |  | X |  | X | X
[81] | X | X | X | X | X | X |
[82] |  |  |  | X |  | X |
[83] |  | X |  | X |  | X | X
[84] |  |  |  | X |  | X | X
[85] |  |  | X | X |  | X | X
[86] | X | X | X | X | X |  |
[87] |  |  | X | X |  | X |
[88] |  |  |  | X |  | X |

This table shows that many related works consider the National Airspace System
(NAS), minimum separation, and complex situations. However, only one of them
considers bad weather conditions. Note that all works that consider the UAS
presence do not integrate them into the NAS, and consequently, all works that
deal with NAS do not include the UAS.

##  5 Open challenges

Although this work aims to address specific topics regarding the UAS
operation, there are many possibilities for the extension of this effort.
Figure 2 depicts several open challenges in UAS integration, simulation,
optimization, and their intersections. In each category, several research
directions are identified and described in detail in this Section.

Figure 2: Open challenges in UAS Integration, Simulation, Optimization, and
their intersections.

###  5.1 UAS Integration

  * •

Measuring the familiarity evolution of different aircraft types throughout the
years: An open challenge in the UAS context refers to the measurement of the
familiarity evolution of different aircraft types (e.g., UAS) as it is
dependent on several human factors and social acceptance. Although some
initiatives have started to investigate this aspect [89] [90], several other
directions require further investigation;

  * •

Priority Establishment of UAS sequencing in the National Airspace System
(NAS): Rather than controlling aircraft from the level of familiarity, an
alternative approach is to consider the priority levels established and
assessed by a standardized scale. One example of prioritized aircraft,
nowadays, is the emergency aircraft [91] [92] [93]. Furthermore, a challenge
is to identify the UAS priority following the priority list assigned to
different aircraft nowadays;

  * •

Automation of some Air Traffic Control (ATC) tasks: A complex open challenge
is the automation of some ATC tasks. For example, approaches such as ATC
Maturity Level (AML), which represents the level of maturity and autonomy of a
system in terms of acting in controlling manned and unmanned aircraft (e.g.,
different approaches for modeling the relationship between the autonomous ATC
with UAS and between the autonomous ATC and MA can be developed) [94] [95]
[96];

  * •

Cognitive impact assessment of UAS integration when emergencies are declared:
Emergencies in the airspace are critical events that need to be carefully
managed [97]. As a result, solutions to deal with these events considering the
UAS presence is a vital open challenge [98] [99] [100].

###  5.2 UAS Simulation

  * •

Applying variations in airspace constraints and parameters: Another future
direction is the development of flexible configurations, e.g., variable CB
sizes and shapes, CB movements, and changes in the minimum separation of the
aircraft depending on their types and on the characteristics of the airspace
(e.g., complexity) [101] [102];

  * •

Evaluation of cognitive impacts of different aircraft types: In the roadmap to
define priorities, the evaluation of cognitive impacts on ATCos is a pivotal
aspect to consider. In fact, this is an evolving area that can significantly
change in the next decades [103] [104]. Thereupon, the design standard
procedures relies on human-centered investigations [105] [106];

  * •

Evaluation of UAS integration in the Advanced Aerial Mobility (AAM): In the
next decades, a new layer of the transportation system is planned to be
deployed and widely used. Advanced Aerial Mobility (AAM) relies on Electric
Vertical Takeoff, and Landing (eVTOL) vehicles [107] [102]. Furthermore, the
integration of autonomous vehicles in this new environment is also a challenge
to be faced in order to ensure future operations are safe and efficient [108]
[109] [110];

###  5.3 UAS Optmization

  * •

Arrival Segment Design Considering Failures in C2 Link: the C2 link enables
the communication between remote pilots and the aircraft [18] [111]. According
to the contingency operations proposed by ICAO, considering a failure in the
communication within the final sector, conducting all aircraft considering the
presence of an independent aircraft is complex. Thus, one open challenge
relies on how the set of aircraft can be conducted throughout the landing
procedure in these situations;

  * •

Optimization of the UAS operation in the TMA: there are several situations
faced in larger scenarios from the airspace operation perspective that can be
considered. For example, the challenge of dealing with several autonomous
aircraft. Examples of open challenges are airspace resilience (e.g., in case
of problems in airports) [112] [113] [114] and impacts of weather conditions
in a long period of time (e.g., decades)[115] [116] [117]. The main idea is to
extend the research conducted in the final sector to a larger and more complex
area, the TMA;

  * •

Development of Vectoring assistant to reduce impact on workload: Vectoring
assistance is a key features in advanced ATC [118] [119] [120]. Although there
are some initiatives under development nowadays, it is important to include
human aspects in those systems when the UAS is part of the operation [121]
[122];

###  5.4 UAS Integration & UAS Simulation

  * •

Automation of some ATC tasks for flexible airspace configurations: In cases of
flexible airspace configuration (e.g., minimum separation, priorities, flight
rules, and disruption), interoperability is essential for ATC assistants.
Examples of such scenarios include abnormal operations with C2Link failures
[123], airport (or vertport in AAM) closure [124] [125], and AAM operations
with aircraft of diverse aerodynamic capabilities (e.g., speed and turning
rate) [126] [127] [128];

  * •

Priority assessment for different aircraft types in AAM: Another challenge
relies on the definition of different priorities in AAM. The integration of
new vehicles (e.g., UAS) hardens the prioritization process due to the lack of
operational history [38] [129]. Thereupon, an assessment of aircraft types and
different scenarios is needed to establish standard priorities [130] [131];

  * •

Cognitive impact assessment of UAS integration in AAM when emergencies are
declared: A simulation effort is also needed in the integration of UAS in AAM
operation [132] [102]. Also, it is pivotal that future directions consider the
analysis of emergencies and abnormal AAM operations, including the UAS.

###  5.5 UAS Integration & UAS Optimization

  * •

ATC assistant for UAS-related disruptions: ATC automated support is important
in several areas of the airspace [133] [134]. Consequently, the integration of
UAS requires new capabilities from these systems. The development of ATC
supporting systems for UAS-related disruption is another important open
challenge;

  * •

Cognitive impact of UAS emergencies considering automated ATC task: Although
some ATC tasks can be automated, the presence of ATCos is paramount [135]
[136]. In this sense, an investigation of how technology and human interaction
in unusual scenarios (e.g., UAS emergencies) [137] [138] is in the scope of
future works;

  * •

Development of Vectoring assistant based on new priority standards: Although
ATC supporting tools are under development, new constraints pose the need for
adaptable systems for future UAS operations [139] [140]. In fact, new
vectoring assistance strategies need to be developed based on future airspace
priority standards.

###  5.6 UAS Simulation & UAS Optimization

  * •

Evaluation of different airspace configurations in disruption: In case of
abnormal events (e.g., emergencies), it is important to understand how the
operation can be optimized [141]. Hence, the evaluation and optimization of
multiple strategies to deal with complex UAS conditions is another open
research challenge.

  * •

Development of Vectoring assistant in the AAM context: Automation of ATC tasks
is challenge for several reasons [142] [143]. For example, standards are under
development, and the daily ATC operation is currently being designed. In this
sense, the development of approaches to deal with possible ATC configurations
and including the UAS is a vital future direction to support safe and
efficient AAM operations;

  * •

Evaluation of cognitive impacts of different aircraft types in AAM: Similarly
to the ATC operation, AAM is expected to have aircraft of multiple
capabilities (e.g., speed) [144]. Considering the UAS presence is critical
since the cognitive impacts on human stakeholders (e.g., ACTos and pilots) can
be significant [145] [146]. Thus, this evaluation fosters the maturity
evolution of UAS operations in AAM;

###  5.7 UAS Integration & UAS Simulation & UAS

  * •

Automation of UAS-enabled AAM control and its interaction with the TMA: The
diverse environment created by the substantial increase of aircraft in the
urban space [147] [148] [149] will require solutions to optimize the
interoperability of the airspace. Solutions that assume the UAS are also
required for safe and efficient future operations;

  * •

Priority assessment in AAM disruptions: AAM is expected to bring various
aircraft to fly simultaneously in the urban environment. Abnormal conditions
can lead to unsafe states and compromise the system performance [29].
Thereupon, it is important to have strategies and standards established for
normal and abnormal operations, considering that these priorities can be
flexible depending on several factors (e.g., UAS presence) [150] [151];

  * •

Social acceptance evolution of UAS integration in AAM operations: UAS is a
disruptive technology to be present in airspace. Consequently, there is a lack
of social acceptance of such aircraft in the airspace (e.g., AAM) [90] [152].
Investigations of how this problem can be mitigated and the most relevant
factors to be addressed represent an open challenge in the UAS context.

##  6 Conclusion

This research presented a comprehensive review of the advancements in the
integration of Unmanned Aircraft Systems (UAS) in the National Airspace System
(NAS) from different perspectives. These contributions include the presence of
UAS in simulation, the final approach, and the optimization of problems
related to the interoperability of such systems in the airspace. Besides, we
also highlighted several open challenges and future directions based on the
contributions analyzed. Finally, we emphasize the benefits that UAS will bring
to society in the next years and reinforce the need for new strategies to deal
with the challenges described in this research.

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