A_Study_on_the_Advancement_of_Intelligent_Military.pdf — page 5

M. Lee et al.: Intelligent Military Drone: A Case Study of a Reconnaissance Drone
(a) Real
(b) Synthetic (Unity)
(c) Augmentation (StyleGAN3)
FIGURE 4. Images of tracked vehicles used to train the YOLOv4-tiny object detection model.
3) When a drone is deployed for scouting in a specific
area to search for enemies, without precise details
about their location, it is essential for the drone to
conduct a comprehensive survey of the entire area it has
been assigned. There’s no easy way around this task.
Therefore, using GNSS to set waypoints, the drone
should cover the area in systematic patterns, such as
grids or spirals, to ensure no part is overlooked. To
navigate to designated waypoints, control over speed
and direction is necessary. This speed and direction
control is achieved by regulating the drone’s rotat-
ing propeller motors. For this purpose, Proportiona l-
Integral-Differential (PID) control, a type of feedback
control system that adjusts the system’s output to the
desired state, is applied. PID control is a fundamental,
efficient, and stable method widely used in various
automatic control systems, including robot control [41].
The formula for PID control is as shown in Equation
(1), where u(t) is the input value at time t related to
the signal or command adjusting the propeller motors.
The error, e(t), as defined in Equation (2), represents
the difference between the target value r(t) and the
output value y(t). For example, during waypoint nav-
igation, r(t) becomes the coordinates of the waypoint
to be reached, and the output value y(t) is the current
coordinate. The control input value u(t) is calculated by
applying proportional, integral, and derivative compo-
nents to reduce this error, that is the difference between
r(t) and y(t). In Equation (1), Kpe(t) represents the
proportional component, which is related to the current
error, Ki
R t
0 e(τ)dτ is the integral component associated
with the accumulated error over time, and Kd d
dt e(t) is
the derivative component linked to the rate of change
of the error. Kp, Ki, and Kd are the parameters for each
of these components, respectively.
u(t) = Kpe(t) + Ki
Z t
0
e(τ)dτ + Kd
d
dt e(t)
(1)
e(t) = r(t) −y(t)
(2)
4) Maintaining a specific altitude also utilizes PID con-
trol. The altitude is measured using an LRF sensor,
and PID control is applied to reduce the difference
between the target value, which is the desired altitude,
and the output value, which is the current altitude
[42]. Maintaining a certain altitude is advantageous as
it ensures the consistent size of the observed object.
Maintaining a uniform object size aids in better de-
tection. This is because, generally, if an object detec-
tion model is trained primarily with small objects for
detecting smaller items, it tends to perform poorly in
detecting larger objects. Conversely, if it is trained with
larger objects to detect bigger items, smaller objects are
not detected as effectively [43]. This trade-off is why
having a consistent object size helps in improving the
overall detection accuracy.
5) In line with this, as illustrated in Figure 5, PID con-
trol is also implemented for tracking when an enemy
tank is detected. During the drone’s reconnaissance and
tracking phases, the camera is consistently oriented
to face straight down. Consequently, the center of the
camera’s image (0.5, 0.5) corresponds to the drone’s
two-dimensional location. When an enemy is detected
at a position like (0.8, 0.8) in the camera view, the
discrepancy from the drone’s location at (0.5, 0.5) is
considered the error. PID control is then engaged to
minimize this error, enhancing tracking accuracy.
FIGURE 5. Physical object tracking using drone camera image.
VOLUME 11, 2023
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This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and 
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3390035
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4