Research paper on indoor drone application with acoustic localization. Paper was accepted to the ISEEE 2019 conference (International Symposium on Electrical and Electronics Engineering).

1. Indoor Drone Application with Acoustic Localization
Ali Anıl DEMİRÇALI, Egemen BALBAN, Abdurrahman YILMAZ,
Gizem Melike CİDAL, Hüseyin ÜVET
Yıldız Technical University, İstanbul, Turkey
alianildemircali1@gmail.com, abdurrahmanyilmaz953@gmail.com
Yıldız Technical University, İstanbul, Turkey,
huseyinuvet@gmail.com
Abstract
This paper investigates the design of a rotary wing unmanned
aerial vehicle (UAV) and the method used for this UAV to
determine its mathematical location indoors. The optimal
method used for the location determination was established
upon research. By reducing the signal loss caused by the
indoor area and with the use of acoustic localization that
measure distance by 3-dimensional vectors with velocity of
ultrasonic sound wave, the determined method allowed the
instantaneous observation of the UAV’s mathematical
location in 3-dimensional space. Furthermore, this method
enabled a balanced and efficient autonomous flight indoors.
1. Introduction
Drones such as unmanned aerial vehicles, can be tracked and have
their mathematical locations determined outdoors, given that they
are equipped with a GPS (Global Positioning System) and 4 or
more satellites. However, this situation cannot be realized in
indoor applications. The main reason for this is that the walls have
a signal reducing effect which causes great deviation in the
process of mathematical location determination.[1][2][4][5]
Hence, it is not possible to track an aerial vehicle and receive data
regarding its location using GPS signals indoors. Other methods
like laser based or wireless and even image processing have also
been tried for location determination for indoor flights. The usage
of wireless communication modules such as Bluetooth or Wi-Fi
for data transaction was attempted as a solution, but it was
unsuccessful due to the fact that the mathematical location
determination had a precision range between 3-5 meters[1][5][12]
Another method is using the laser technique with LIDAR sensor
or SLAM for mapping. However these systems having extremely
high prices, besides that in an exceptional case for aerial vehicle,
the laser module would have an incorrect angle and the mapping
process would lose precision. Therefore, this method was found
to be insufficient indoors. .[1]
In the previous studies, indoor drone flight has been attempted,
but there have been no studies on its warehouse applications.[1]
Most of the studies up to now were on laser-based mapping and
communication protocols. Land vehicles, specifically forklifts
and similar vehicles, are widely used in the warehouse studies.
However, the usage of forklifts and similar vehicles pose a threat
to human life in warehouses considering the fact that several big
and heavy products are stored.
In this paper, the mathematical location of a drone flying in the
halls of a warehouse is determined using acoustic localization,
which is the method of measuring distances using ultrasonic
sound waves. By receiving the required data using the
aforementioned method, it will be possible for the aerial vehicle
to fly efficiently and autonomously in warehouses for security,
counting products or other purposes. The solution that this work
focuses on is an autonomous aerial vehicle capable of counting
the products on the shelves inside the warehouse.
2. Mathematical Model
2.1 Rotation Matrixes
The relationship between rotations and axes is shown in Fig.1.
Deviation, pitching and wobble rotation matrices are as follows.
𝑅 𝐺
𝐷
refers to the rotation matrix of the moving axis on the fixed
axis.
Deflection Rotation Matrixes:
𝑅 𝐺
𝐷(𝜓) = [
𝑐𝑜𝑠 𝜓 𝑠𝑖𝑛 𝜓 0
−𝑠𝑖𝑛 𝜓 𝑐𝑜𝑠 𝜓 0
0 0 1
] (1)
Rolling Rotation Matrixes:
𝑅 𝐺
𝐷(θ) = [
𝑐𝑜𝑠 θ 0 −𝑠𝑖𝑛 θ
0 1 0
𝑠𝑖𝑛 θ 0 𝑐𝑜𝑠 θ
] (2)
Pitching Rotation Matrixes:
𝑅 𝐺
𝐷(ϕ) = [
1 0 0
0 𝑐𝑜𝑠 ϕ 𝑠𝑖𝑛 ϕ
0 −𝑠𝑖𝑛 ϕ 𝑐𝑜𝑠 ϕ
] (3)
The matrices (1), (2), (3) are multiplied to show the relationship
between the fixed axis and the body axis with a single matrix.
𝑅 𝐺
𝐷
= 𝑅 𝐺
𝐷(𝜓)𝑅 𝐺
𝐷(θ)𝑅 𝐺
𝐷(ϕ)

2. 𝑅 𝐺
𝐷
=
[
c𝜓 cθ c 𝜓 s θ s ϕ − s 𝜓 c ϕ c𝜓 sθ cϕ + s𝜓 sϕ
s𝜓 cθ s𝜓 s θ s ϕ + c 𝜓 c θ s𝜓 sθ cϕ − c𝜓 sϕ
−𝑠θ c θ s ϕ cθ cϕ
]
(4) (c: Cos, s: Sin)
2.2 Equations of Motion
6 To obtain the dynamic model of the motorized UAV, Newton
Euler connection will be used to express the forces and torques
acting on a rigid body.
Newton- Euler Rquations:
The Newton- Euler relation allows us to write Euler 's 2 equations
of motion for a rigid body as a single equation with 6 variables.
[
𝑚𝐼3𝑥3 03𝑥3
03𝑥3 𝐼
] [ 𝑉̇ 𝐺
ѡ̇ 𝐺
] + [ѡ 𝐺 × 𝑚𝑉 𝐺
ѡ 𝐺
× 𝐼ѡ 𝐺 ] = [ 𝐹 𝐺
𝜏 𝐺 ] (5)
In matrixes equations , 𝑚 [kg] mass, 𝐼 [𝑁𝑚𝑠2
] moment of inertia,
𝑉 𝐺
= [ 𝑢 𝑣 𝑤 ] [m/s] linear speed on frame axis, ѡ 𝐺
=
[ 𝑝 𝑞 𝑟 ][𝑟𝑎𝑑/𝑠] angular speed on frame axis, 𝐹 𝐺
[𝑁] Forces on
UAV, 𝜏 𝐺[𝑁𝑚] Torques on UAV.
𝐼3𝑥3 = [
𝐼 𝑥𝑥 0 0
0 𝐼 𝑦𝑦 0
0 0 𝐼𝑧𝑧
]
shows the moment of inertia matrix.
If we show the force and torque equations according to these
statements
𝐹 𝐺 = 𝑚𝐼3𝑥3 𝑉̇ 𝐺 + ѡ 𝐺 × 𝑚𝑉 𝐺 = [
𝑚𝑢̇
𝑚𝑣̇
𝑚𝑤̇
] +
[
0 −𝑟 𝑞
𝑟 0 −𝑝
−𝑞 𝑝 0
] [
𝑚𝑢
𝑚𝑣
𝑚𝑤
] (6)
𝜏 𝐺
= 𝐼ѡ̇ 𝐺
+ ѡ 𝐺
× 𝐼ѡ 𝐺
(7)
If angular and linear accelerations are drawn from these equations
[
𝑢̇
𝑣̇
𝑤̇
] = [
𝑟𝑣 − 𝑞𝑤
𝑝𝑤 − 𝑟𝑢
𝑞𝑢 − 𝑝𝑣
] +
[
1
𝑚
𝐹𝑥
𝐺
1
𝑚
𝐹𝑦
𝐺
1
𝑚
𝐹𝑧
𝐺
]
(8)
2.3 Forces and Torque
(8) and (9). The expression of Force and Torque in the
expressions in equality will be obtained.
2.3.1 Forces
The forces acting on the UAV body are examined and shown
below in Figure 5.
Gravity:
Since it acts only in the opposite direction to the z-axis, it can be
expressed by the following matrix.
Fgravity
G
= RD
G
[
0
0
-mg
] = [
mgsin θ
-mgcos θ sin ϕ
-mgcos θ cos ϕ
] (10)
Thrust:
6 The total impulse force generated by the propeller is shown in
the figure below. refers to the impulse constant.. 𝑏[𝑁𝑠2]refers to
the impulse constant..
𝐹𝑡ℎ𝑟𝑢𝑠𝑡
𝐺
= 𝑏 ∑ Ω𝑖
2
6
𝑖=1
(11)
Air Reesistance:
The negative force that the propeller is exposed to by the air when
moving at high speeds can be expressed as follows. It is
proportional to the square of the speed.
Fair
G
=
[
-
1
2
CAxρu2
-
1
2
CAyρu2
-
1
2
CAzρu2
]
(12)
𝐶 Express the air resistance, 𝐴 𝑥, 𝐴 𝑦, 𝐴 𝑧[𝑚2
] Express the
sectional area, 𝜌[𝑘𝑔/𝑚3
] Express the air density.
Torque:
Deviation from engine speed differences, rollinf and pitching
movements occur. The torques that cause these movements are
shown below. 𝑑[𝑁𝑚𝑠2] express the drag forces.
Roll Torque:
𝜏 𝑟𝑜𝑙𝑙𝑖𝑛𝑔 = 𝑏𝑙
√3
2
(Ω2
2
+ Ω3
2
− Ω5
2
− Ω6
2
) (13)
This is due to the fact that the angle made with the eksen
√3
2
x -
axis in the torque equation is 60 °. This is shown in FIG. In Figure
x is the distance of the motor from the intersection of the x and y
axis. 𝑏[𝑁𝑠2] refers to the impulse constant
Fig. 1. The shown of the direction the rotors. (1,3,6) the
rotors are the green ones show that they turn to clockwise,
(2,4,5) the blue ones show that the rotors turn to counter
clock wise
Pitching Torque:
𝜏 𝑝𝑖𝑡𝑐ℎ𝑖𝑛𝑔 = 𝑏𝑙(−Ω1
2
+ Ω4
2
+
1
2
(−Ω2
2
+ Ω3
2
+ Ω5
2
−
Ω6
2
) ) (14)
The expression
1
2
in the equation stems from the fact that the
angle of the 2,3,5 and 6 motors with the axis is 30°
Yawwing Torque:
𝜏 𝑑𝑒𝑓𝑙𝑒𝑐𝑡𝑖𝑜𝑛 = 𝑑(−Ω1
2
+ Ω2
2
−Ω3
2
+ Ω4
2
− Ω5
2
+ Ω6
2
)
(15)
The expression d in the equation shows the drift coefficient.
Gyroscopic Effect of Propellers:
The rotation of the propellers has a gyroscopic effect on the UAV.
𝜏 𝑔𝑦𝑟𝑜𝑠𝑐𝑜𝑝𝑒 = [
−𝐽𝑟θ̇Ωr
−𝐽𝑟ϕ̇ Ωr
0
] (16)

3. 𝐽𝑟 [𝑁𝑚𝑠2]Moment of inertia of propellers, Ωr[𝑟𝑎𝑑/𝑠] Express
total impeller speed.
Reverse Deflection Torque:
Because of the different acceleration of the propellers, reverse
torque occurs in the deflection direction. Ωr
̇ [𝑟𝑎𝑑/𝑠2] shows the
angular acceleration.
τreverse = [
0
0
JrΩr
̇
] (17)
2.4 Force and Torque Equations
The force and torque equations we obtained, if we use the
equations (8) and (9).
[
𝑢̇
𝑣̇
𝑤̇
] =
[
𝑟𝑣 − 𝑞𝑤 + 𝑔sin θ −
1
2𝑚
𝐶𝐴 𝑥 𝜌𝑢2
𝑝𝑤 − 𝑟𝑢 − 𝑔cos θ sin ϕ −
1
2𝑚
𝐶𝐴 𝑦 𝜌𝑢2
𝑞𝑢 − 𝑝𝑣 − 𝑔cos θ cos ϕ +
1
m
𝐹𝑖𝑡𝑘𝑖 −
1
2𝑚
𝐶𝐴 𝑧 𝜌𝑢2
]
(18)
[
𝑝̇
𝑞̇
𝑟̇
] =
[
𝐼 𝑦𝑦−𝐼 𝑧𝑧
𝐼 𝑥𝑥
𝑞𝑟 +
1
𝐼 𝑥𝑥
𝜏 𝑦𝑎𝑙𝑝𝑎𝑙𝑎𝑚𝑎 −
1
𝐼 𝑥𝑥
𝐽𝑟θ̇Ωr
𝐼 𝑧𝑧−𝐼 𝑥𝑥
𝐼 𝑦𝑦
𝑝𝑟 +
1
𝐼 𝑦𝑦
𝜏 𝑦𝑢𝑛𝑢𝑠𝑙𝑎𝑚𝑎 +
1
𝐼 𝑦𝑦
𝐽𝑟ϕ̇ Ωr
𝐼 𝑥𝑥−𝐼 𝑦𝑦
𝐼 𝑧𝑧
𝑝𝑞 +
1
𝐼 𝑧𝑧
𝜏 𝑠𝑎𝑝𝑚𝑎 +
1
𝐼 𝑧𝑧
𝐽𝑟Ωr
̇
]
(19)
𝐹𝑡ℎ𝑟𝑢𝑠𝑡
𝐺
= 𝑏 ∑ Ω𝑖
2
6
𝑖=1
𝜏 𝑟𝑜𝑙𝑙 = 𝑏𝑙
√3
2
(Ω2
2
+ Ω3
2
− Ω5
2
− Ω6
2
)
𝜏 𝑝𝑖𝑡𝑐ℎ = 𝑏𝑙(−Ω1
2
+ Ω4
2
+
1
2
(−Ω2
2
+ Ω3
2
+ Ω5
2
− Ω6
2
) )
𝜏 𝑦𝑎𝑤 = 𝑑(−Ω1
2
+ Ω2
2
−Ω3
2
+ Ω4
2
− Ω5
2
+ Ω6
2
)
Ωr = −Ω1 + Ω2 − Ω3 + Ω4 − Ω5 + Ω6
2.5 Battery Selection
𝐼 = 𝐶𝑟 ∗ 𝐸𝑟
𝐶𝑟 = 𝐼 𝐸𝑟
Er = Rated energy stored in Ah (rated capacity of the battery
given by the manufacturer)
I = current of charge or discharge in Amperes (A)
Cr = Discharge rate of the battery
equation to get the time of charge or charge or discharge "t"
according to current and rated capacity is:
𝑡 = 𝐸𝑟 𝐼
t = time, duration of charge or discharge (runtime) in hours
Relationship between Cr and t ;
𝐶𝑟 =
1
𝑡
𝑡 =
1
𝐶𝑟
Matematical Localization
In this paper, drone move by the hall and localization of drone
measure with the vectoral distance to the microphones. At first
drone locate to the hall and microophones locations are known.
After that while drone move through the hall drone measure the
distance with sound velocity. So 3 different equations has
obtained below.
The referance distance of drone to the first microphone is |𝐴|
The referance distance of drone to the second microphone is |𝐵|
The referance distance of drone to the third microphone is |𝐶|
Distance equations:
𝐴2
= 391,572
= 𝑖2
+ 𝑗2
+ 𝑘2
𝐵2
= 402,192
= 𝑖2
+ 𝑏2
+ 2𝑗𝑏 + 𝑗2
+ 𝑘2
+ 𝑐2
+ 2𝑘𝑐
𝐶2 = 432,92 = 𝑖2 + 𝑗2 + 𝑘2 + 𝑎2 + 2𝑘𝑎
If you solve the equations.
|𝑖| = 370.01
|𝑗| = 119,95
|𝑘| = 44,99
Fig. 2. Acoustic Localization Description
3. Component Selection
The drone that fly autonomously and read the barcodes on the
packages located on shelves throughout the warehouse hallways.
It must have a certain payload, because in addition to common
drone equipments such as battery, flight controller, motors and
electronic speed controllers, there is also a camera and a camera
lens for streaming or image processing, and there are movement
sensors located on its top and front for maintaining a safe
distance. Besides, the transmitter that will produce the ultrasonic
sound waves for location determination is also one of the
beneficial loads that will be located on the drone. Design of the
drone shown below in Fig.3. The aforementioned loads are given
in Table 1 along with their weights.

4. Fig. 3. Warehouse stock counting drone
Table 1. System Needs and Payloads
Material Qty Weight(gr) Total Weight(gr)
Li-Po Battery 2 782 1570
Frame 1 810 810
Motor 6 97 582
Camera Lens 1 245 245
ESC 6 28 168
Propeller 6 15 90
Camera 1 80 80
Flight Controller 1 75 75
Cables 1 75 75
Transmitter 1 60 60
Altitude Sensor 1 27 27
3DR Receiver 1 17 17
Power Module 1 16 16
PCB 1 15 15
Streaming Cables 1 15 15
Total 3845
4. Warehouse Solution
The ultrasonic sound waves transmitted from the drone are
received by the microphones located throughout the warehouse
halls. By measuring the time it takes for the transmitted sound
waves to be received by the microphones with known locations,
the mathematical location of the drone is determined. This
method is called acoustic localization. With this method, the
sensors can be tracked within an error range of 3-4 cm due to the
motion of the drone, and it is seen that these values are at a
sufficient level for a system that operates instantaneously. The
drone system’s operation method inside the hall is shown in Fig.4.
Fig. 4. Ultrasonic sound wave transmitter and receivers location
on the hallway.The hallways dimensions is 120m length and 50m
width (The curves represent the ultrasonic waves)
precautions are taken. An altitude sensor is used to maintain a safe
distance between the drone, the floor and the ceiling in
unexpected situations. In order to balance the distance between
the shelves and maintain a safe location, the distance between the
drone and the shelves is limited using a laser distance sensor.
Fig. 5. Application Description
In operation drone fly betwenn shelves and follow a path to read
the barcodes on the boxes. The path that drone followed for this
opeation shown below in the Fig.6 with yellow fill and black
border.
Fig. 6. The yellow path that drone followed and read the
barcodes
5. Simulation and Experiments
5.1 Static Analysis
Drone designed for the warehouse application, after some
simulations and calculations for mechanic durability and software

5. accuracy. The stress analysis of the system shown below in Figure
7 and the strain analysis also shown in Fig.8.
Fig.7. Stress Analysis (Isometric View)
Fig.8. Stress Analysis with Deformation (Side View)
5.2 Experiments
For the localization accurasy, we located 4 reference microphone
and calculated that drones position and its shown in Fig.9. The
green circles are represent the reference microphones and the
hollow green circle represent the drone. We calculated the
position and made that simulation. After that we move the drone
to new attitude and the path that drone followed shown in the
Fig.10. They showed that the calculation and the simulation is
accurated for localization opeation.
Fig.9. Software Interface that shows the matematical
location microphones and the drone (The green ones are
microphones and the unfilled blue circle is the drone.)
Fig.10. Showing of the moving of the drone (Blue prints are the
path that drone follows.)
6. Conclusions
It is a difficult case for a drone to fly autonomously and
controlled stable while this drone is being designed for warehouse
product counting and this system’s PID setting are being made.
Since the warehouse is an indoor area, its walls absorb and scatter
GPS signals. The deviation or absorption of the GPS signals in
turn cause an interruption in the process location determination
and a high error percentage. Therefore, using GPS signals as a
method for location determination is not suitable for an
autonomously flying drone operating indoors. In this study,
however, the drone system used the acoustic localization method
with ultrasonic sound waves for location determination, and the
instantaneous mathematical location of the drone was determined
in 3-dimensional space with an error margin of 4-5 cm.
Furthermore, it was observed that other methods that were used
to count products in warehouses pose a threat to workplace
security and this study aimed to solve this issue with a stable
drone system. For security, the aerial vehicle was equipped with
distance sensors, an altitude sensor, and an emergency stop relay
which is activated if the security barrier is breached. Thus, the
produced autonomous drone system minimizes risks in the
warehouse. In the future, this system can turn into a completely
autonomous one that does not require any supervision, and it can
be used for product counting or other purposes in warehouses of
any kind, whether it poses a low risk, or a high risk with big and
heavy product packages. Furthermore, it may surely clear the path
in front of multiple drone controlling systems..
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