=Paper=
{{Paper
|id=Vol-3732/paper10
|storemode=property
|title=Simulation of wind-related hazards avoidance when UAS operation in urban environment
|pdfUrl=https://ceur-ws.org/Vol-3732/paper10.pdf
|volume=Vol-3732
|authors=Yuliya Averyanova,Maxim Ivanytskyi,Bohdan Shershen,Yevheniia Znakovska,Yaryna Zhdanova
|dblpUrl=https://dblp.org/rec/conf/cmse/AveryanovaISZZ24
}}
==Simulation of wind-related hazards avoidance when UAS operation in urban environment==
https://ceur-ws.org/Vol-3732/paper10.pdf
Simulation of wind-related hazards avoidance when UAS
operation in urban environment
Yuliya Averyanova1,†, Maxim Ivanytskyi1,†, Bohdan Shershen1,†, Yevheniia
Znakovska1,∗,† and Yaryna Zhdanova2,†
1 National Aviation University, Liubomyra Huzara Ave., 1, Kyiv, 03058, Ukraine
2 University of Vic-Central University of Catalonia, Ctra. de Roda, 70, Vic, 08500, Spain
Abstract
Nowadays unmanned aircraft systems (UASs) are planned to be used in many branches of
people's activity and fulfill different tasks. Many of the tasks including cargo delivery, photo and
filmmaking, future transportation, monitoring, different security applications, and others are,
obviously the subject of urban flying. In this case, the different constraints should be taken into
account. Some of the constraints are connected with weather-related hazards. In this paper, we
consider the in-flight flight trajectory correction to avoid temporary dangerous areas for flight.
We present a decision support system that can be used by remote pilots or implemented as a
component of an onboard flight control system for hazard detection and operative conflict
resolution. The system operation is based on information fusion from the network of distributed
sensors additionally to the general set of data. The simulation of proposed trajectory corrections
is shown and discussed. Also, we discuss the potential communication channels for operative
information sharing and dissemination.
Keywords
air navigation, navigation, aviation, meteorology, UAS, simulation, trajectory correction,
decision-making1
1. Introduction
The prospects of drones utilizing in many branches of human activity and providing
innovative aerial services [1] require consideration of a series of requirements [2] and
restrictions to ensure the safe integration of unmanned aircraft system (UAS) into shared
airspace [1, 3]. The concept of U-space as the area of airspace where UAS flights and
operations are allowed [1, 3] considers the importance of the availability of information for
all participants of air traffic. In [3] the outline of the common information services is shown.
One of the requirements of the services is live operational data exchange to provide U-space
CMSE’24: International Workshop on Computational Methods in Systems Engineering, June 17, 2024, Kyiv,
Ukraine
∗ Corresponding author.
† These authors contributed equally.
ayua@nau.edu.ua (Yu. Averyanova); maksumiljano2002@gmail.com (M. Ivanytskyi);
bogdansersen50@gmail.com (B. Shershen); zea@nau.edu.ua (Ye. Znakovska); yaryna.zhdanova@uvic.cat
(Y. Zhdanova)
0000-0002-9677-0805 (Yu. Averyanova); 0009-0007-0601-3901 (M. Ivanytskyi); 0000-0001-8572-1963
(B. Shershen); 0000-0002-9064-6256 (Ye. Znakovska); 0000-0002-9330-6679 (Y. Zhdanova)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
services and ensure flight safety. Some of the factors that influence UAS flight safety are
weather and weather-related phenomena. This can be especially important when flying
over populated areas and in urban areas. Flying in urban areas is associated with additional
unexpected and rapid weather-related hazards that are connected with higher
irregularities of the underlying surface. These include buildings of different configurations
and other constructions and their proximity, urban heat islands [4], and air pollutants. etc.
In [5] it is mentioned that urban microclimates are the most complex and least understood.
This is because of the heterogeneity of the constructions including industrial ones, other
roughness elements, and the turbulence that can be present even under calm wind
conditions and anthropogenic heat sources, in addition to other complex meteorological
processes. Moreover, the climate change study [6] shows an increase in turbulence events
over the globe. The weather-related risks for UAS are discussed in papers [7, 8, 9]. Paper
[10] focuses also on the weather constraints for particular missions performed with UASs.
In this paper, we present the decision support system (DSS) that helps to evaluate
weather-related risks when UAS flight planning. The system also can be used as a source of
operative information for in-flight trajectory correction to avoid potentially hazardous
regions. The simulation of the trajectory correction is presented and discussed. Also, the
simulations to study the possibility of using ZigBee technology for information
dissemination are presented and discussed. The presented system can be used by remote
pilots or implemented as a component of an onboard flight control system for hazard
detection and operative conflict resolution.
2. DSS overview and flight trajectory correction simulation
Decision-support systems (DSS) can be an important component of the air transportation
subsystem [11], that considers the UASs operations, as they allow to collect and process the
vast volumes of information and decrease the overload of the remote pilots.
In the paper [12] the general architecture of the DSS that collects, processes, and
proposes the decisions based on the risk-oriented approach [13, 14] is presented. Different
scenarios of remote pilot operations were simulated using the proposed system and results
are presented and discussed in [15]. The automation in decision-making support with the
developed system is discussed in [16]. The DSS operation can be depicted using the diagram
shown in Figure 1.
Weather-related risks (Figure 1) are assessed by taking into account:
• Mission, as weather can significantly influence the ability to perform a particular
task and has no influence on the general possibility of performing flight and other
missions.
• The area of flight – urban or rural, over people assemble, etc. can be considered as
an additional risky factor.
• Flight parameters can be connected with a particular mission as intended time,
distance and with UAS characteristics.
• Additional apparatus placed at UAS to help in mission realization as they can be
affected by weather.
• Information about current meteorological conditions and phenomena (used at the
stage of preparation for the flight and mission).
• A type of unmanned aircraft and related characteristics.
• Data about current meteorological conditions and phenomena and dynamically
changing weather conditions.
General
meteorological
information
including
Aviation forecasts Decision-making
support: remote
Mission and
pilot warning and
related flight
recommendations
parameters;
type of UAS and Risk assessment
characteristics;
area of flight; Trajectory
additional sensors correction:
proposed
Constraints trajectory
alternatives
Operative weather
information
Figure 1: DSS general architecture.
The DSS can be used for automated flight trajectory correction (Figure 1). The important
and remarkable feature of the system is that the fused information from the network of
distributed and dynamic sensors is used to obtain operative information about weather
hazards.
Results of UAS flight trajectory correction are presented in Figure 2. We considered the
real meteorological conditions over the area indicated in the built-in map of the DSS.
The interface of the DSS shown in Figure 2 allows to choose the area of flight, mission,
type of unmanned aircraft, and flight parameters. The characteristics that correspond to
particular UAS are indicated and then used by the system to assess the weather-related
risks for general flight. The information about current weather conditions is automatically
collected and indicated in the panel of the DSS. There is an option to switch panels to
demonstrate the operative weather. It is proposed to be informed about the present state
of the atmosphere using data from the sensors placed on the other unmanned aircraft that
perform missions in the area of intended flight. The information from the unmanned aircraft
that perform flight in the area indicated in Figure 2 is shown in Table 1. The red color covers
the forbidden flight zones. However, thanks to the technology of geo-zoning [17, 18] and the
protection of sensitive areas, a route was modelled that demonstrates the effective
execution of the UAV mission.
Figure 2: DSS interface and simulated trajectory options.
We can see from Figure 2 shows that the initial flight path passes through an area with
buildings and the mission involves flying among these buildings. The blue trajectory shows
the planned flight route that will allow the operator to fly and obtain photo/video data
about the area. To calculate low-level turbulence, we use the Bellman-Fort model [19]. The
model takes into account wind speed and the size of structures in the wind path. DSS
identifies the flight as unsafe in the area near buildings and offers alternative flight options.
The planned flight trajectory is indicated with a blue line. The system identifies the flight
in point 7 as dangerous because of increased wind and mechanical turbulence. This can be
seen in Table 1. The two alternatives are proposed. In this particular simulation, the
trajectory was corrected based on Bellman's algorithm [20, 21].
The total length of the route is calculated using the formula (1):
𝑛
𝑑 = ∑ √(𝑥𝑖+1 − 𝑥𝑖 )2 + (𝑦𝑖+1 − 𝑦𝑖 )2 + (𝑧𝑖+1 − 𝑧𝑖 )2 (1)
𝑖=1
where (𝑥𝑖+1 − 𝑥𝑖 ), (𝑦𝑖+1 − 𝑦𝑖 ) and (𝑧𝑖+1 − 𝑧𝑖 ) are the differences between the x, y and 𝑧
coordinates of the neighboring points of the route; 𝑛 is the number of selected control points
on the route, which demonstrates the change of the UAV's heading.
The x and y coordinates form the foundational elements of the coordinate system,
allowing precise tracking of the UAV's location within the designated flight sector. Here, the
x coordinate represents latitude, determining the UAV's position in the North-South
direction, while the y coordinate represents longitude, indicating the UAV's position in the
east-west direction.
Table 1
Information from the drone
Point Planning Flight Distance Alternative Flight Distance 1 Alternative Flight Distance 2
Wind, Speed, Temp., °C Wind, Speed, Temp., °C Wind, Speed, Temp., °C
m/s m/s m/s m/s m/s m/s
1 1.4 8.1 12 1.4 8.3 12 1.4 10.1 12
2 1.5 8.5 12.2 1.5 8.5 12.2 1.5 10.3 12.2
3 1.8 8.4 12.2 1.8 8.4 12.2 1.8 10.4 12.2
4 1.6 8.8 12.2 1.6 8.8 12.2 1.6 10.8 12.2
5 1.5 8.6 12.2 1.5 8.6 12.2 1.5 10.6 12.2
6 1.5 8.1 12.3 1.5 8.1 12.3 1.5 9.1 12.3
7 7 8.8 12.2 1.7 8.8 12.2 1.7 9.8 12.2
8 2 8.9 12.2 2 8 12.2 2 8.8 6.9
9 2.05 8.3 12.3 2.05 8 12.3 2 9.1 7
10 2.2 9.1 12.4 2.2 8.1 12.4 1.3 10.3 6.6
11 2 8.9 12 2 8 12 1 10.4 6.3
12 2 6.2 12.1 2 9 12.1 2 2.4 7
13 1.3 8.5 12.1 1.3 9.2 12.1 - - -
14 1 3.2 12.4 1.6 6.1 12.4 - - -
15 - - - 1.2 3.4 12.4 - - -
In addition to the horizontal positioning provided by the x and y coordinates, the
coordinate system employed in UAV missions incorporates a crucial vertical dimension: the
flight altitude. This vertical component is essential in aviation, as it not only ensures safe
navigation but also allows for mission-specific adjustments based on terrain, obstacles, and
airspace regulations.
In the software environment, it is implemented as a function that takes arrays of 𝑥 and 𝑦
coordinates of the route points and returns the total length of the route based on its
trajectory in space [22, 23]. In the software environment, this formula is implemented as a
function that takes each individual section of the route length as a separate full-fledged
flight route, which demonstrates the uniqueness and peculiarity of the mission, which has
other micro-missions.
The flight parameters for each of the options and intended flight trajectory are also
shown in the lower part of the interface shown in Figure 1. This information can be used for
final decision-making depending on the chosen priorities [24, 25]. The flight parameters
information and atmospheric conditions information are summarized in Table 1. Analyzing
the flight parameters of the intended and proposed options it is possible to say that system
recommendations allow performing flight operations despite the presence of weather-
hazardous areas along flight trajectory with rather no marked loss in flight efficiency (fuel
consumption, flight time, etc.).
3. Considerations on communication to share operative information
The presented system operation is based on operative meteorological information sharing
between mobile participants of air traffic in U-space and stationary participants (remote
pilots, control stations, flight controllers) [26]. Therefore, the consideration of modern
technologies that can be used for weather data dissemination is an important task. We chose
to analyze the ZigBee wireless technology [27] taking in mind the advantages of this
protocol [28]: rather low cost and ability to connect a larger number of devices, security,
and simplicity in implementation, reliability, and mesh topology when communication that
give the possibility to organize communication between devices without central node.
For this purpose, the signal power was calculated with signal losses due to propagation
in free space and other factors affecting its propagation. The distance from the transmitter
to each point is calculated using the formula for the distance between two points in three-
dimensional space. (0:0:0) is the origin of coordinates for considered area (1).
The signal power 𝑃𝑟 at each point is calculated using a formula that includes distance
loss, obstacle loss, and other losses:
𝑑𝑡𝑜𝑡𝑎𝑙 − 𝑑 4𝜋
𝑃𝑑 = 𝑃𝑡 − (20 𝑙𝑜𝑔10 ( ) + 20𝑙𝑜𝑔10 (𝑓) + 20𝑙𝑜𝑔10 ( )), (2)
𝑑𝑡𝑜𝑡𝑎𝑙 𝑐
where 𝑃t = 160 dBm for the example in Figure 3 is the transmitter power; 𝑑𝑡𝑜𝑡𝑎𝑙 = 96.0469
m is the distance between the points of location of transmitter and the receiver; 𝑑 from 0 m
to 96.0469 m is the distance from the point to the receiver; 𝑓= 2.4 GHz is the frequency of
the signal; 𝑐= 3·108 is the speed of light.
The formula for calculating interference from Wi-Fi takes into account the influence of
signals from other Wi-Fi routers on the ZigBee signal. Usually, this interference is taken into
account as additional power loss due to interference:
4𝜋
20𝑙𝑜𝑔10 (𝑑𝑟𝑜𝑢𝑡𝑒𝑟 )+20𝑙𝑜𝑔10 (𝑓)+20𝑙𝑜𝑔10 ( )
𝑐 )
𝑃𝐿𝑊𝑖−𝐹𝑖 = 10𝑙𝑜𝑔10 (𝑛𝑟𝑜𝑢𝑡𝑒𝑟𝑠 10( 10 ), (3)
where 𝑃𝐿𝑊𝑖−𝐹𝑖 is the loss of power from interference from Wi-Fi; 𝑛𝑟𝑜𝑢𝑡𝑒𝑟𝑠 = 20 is the number
of affected Wi-Fi routers; 𝑑𝑟𝑜𝑢𝑡𝑒𝑟 = from 0 m. to 96.0469 m. is the distance to the Wi-Fi router,
which is taken into account in the calculations; 𝑓= 2.4 GHz is the frequency of the Wi-Fi
signal; 𝑐= 3·108 is the speed of light.
The average ZigBee signal loss per wall is determined experimentally depending on the
material of the wall, its thickness, the presence of metal elements in the wall and other
factors. Common practice is to use values between 3 and 6 dB for internal walls and up to
12 dB for external walls. Such values may vary. The variation depends on some specific
conditions and features of the premises. We will use the largest average values
𝑃𝐿𝑤𝑎𝑙𝑙 = 𝑃𝐿𝑒𝑥𝑡 𝑛𝑒𝑥𝑡 + 𝑃𝐿𝑖𝑛𝑡 𝑛𝑖𝑛𝑡 , (4)
where 𝑃𝐿𝑒𝑥𝑡 up to 12 dB is the power loss from the external wall; 𝑛𝑒𝑥𝑡 = 4 is the number of
external walls; 𝑃𝐿𝑖𝑛𝑡 values between 3 and 6 dB is the power loss from the internal wall;
𝑛𝑖𝑛𝑡 = 2 is the number of internal walls.
If there is an initial signal power (𝑃𝑡 ) to calculate power loss due to distance (𝑃d ), power
loss due to walls (𝑃𝐿𝑤𝑎𝑙𝑙 ) and power loss due to Wi-Fi (𝑃𝐿𝑊𝑖−𝐹𝑖 ), then total power loss
(𝑃𝐿𝑡𝑜𝑡𝑎𝑙 ) can be calculated by formula:
𝑃𝐿𝑡𝑜𝑡𝑎𝑙 = 𝑃𝑡 − 𝑃𝐿𝑤𝑎𝑙𝑙 − 𝑃𝐿𝑊𝑖−𝐹𝑖 − 𝑃𝑑 , (5)
where 𝑃𝐿𝑡𝑜𝑡𝑎𝑙 is the total loss of signal power in decibels, which includes losses due to walls,
Wi-Fi and distance.
This value indicates how much the signal power will be reduced from the transmitter to
the receiver due to all these losses. The greater the value of 𝑃𝐿𝑡𝑜𝑡𝑎𝑙 is, the lower the signal
power at the receiver will be. In Figure 3, the signal attenuation comparison between line-
of-sight propagation and under the presence of buildings and Wi-Fi influence is shown.
Figure 3: Comparison of ZigBee signal attenuation in line-of-sight and building obstacles
and noise from Wi-Fi routers.
It is possible to see from Figure 3 that Zig Bee provides rather good communication in
the line-of-sight conditions. We observe rather slight attenuation at a distance of 96 m.
However significant influence is made by Wi-Fi routers. In this case, the relatively limited
range for communication is provided when Zig-Bee technology is utilized. Therefore, the
additional amplification of the signal is required.
There are also some restrictions connected with the use of ZigBee technology. These
include the relatively limited range of communication. Therefore, it was our motivation to
investigate the restriction and estimate the possibility of ZigBee technology for operative
weather data sharing for decision support of UAS operators or automated flight trajectory
correction.
4. Conclusions
In this paper, we considered and analyzed the operation of DSS which utilizes the
distributed network of UASs to obtain operative information about weather-related
hazards along the flight path. We presented the simulated example of DSS operation and
options for operative flight path correction in urban environment. The simulation results
demonstrate the consistency of the system to give recommendations concerning the
planned mission, type of the unmanned aircraft and its characteristics, and planned area
taking into account the geo-fencing and sensitive areas.
The system can be used by remote pilots or implemented as a component of the onboard
flight control system for hazard detection and operative conflict resolution. The proposed
method of obtaining information about present state of the atmosphere and considered
system can be used as the basis for dynamic geo-fence implementation. The simulation of
proposed trajectory corrections was done and discussed. Simulation results have shown the
possibility of operative correction of UAS flight taking into account the mission, area, and
type of aircraft. Therefore, different decisions can be proposed for different scenarios. Some
discussions on the communications between distributed networks of air traffic participants
were done.
5. Future Research
Future research will broaden the current study into the context of modern trends in urban
planning. Such trends might include vertical farms, green and smart buildings. First,
multistory vertical farms can create line-of-sight obstructions in addition to intense light
emissions caused by artificial LED lighting. Thus, when facade glazing is used to enclose
growing premises in such buildings, it is expected that it will cause a significant visual
lightning obstacle for UAS.
Second, green roofs and facades are commonly used for vertical farms and many other
types of buildings, which creates a significant vegetation area on the external walls and
roofs. Such a type of surface can absorb, scatter, or reflect radio waves, causing signal
degradation and distortion different from commonly used building materials.
Finally, smart buildings, including vertical farms, typically incorporate a wide range of
IoT devices and sensors that collect data related to building management, indoor and
outdoor environmental conditions. In turn, such information exchange could be beneficial
for UAS-based smart city applications. Modern urban planning trends will be further
studied to determine their influence on the choice of telecommunication technologies, data
transmission protocols, information exchange, and possibilities of smart city implications.
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