A_Study_on_the_Advancement_of_Intelligent_Military.pdf — page 10

M. Lee et al.: Intelligent Military Drone: A Case Study of a Reconnaissance Drone
including altitude maintenance and enemy tracking, depends
on a traditional feedback control system, specifically a PID
controller. Lastly, to provide high confidence level as useful
information for those making attack decisions, the intelligent
drone utilizes a reinforcement learning neural network. This
enables autonomous movement and camera angle adjustment.
This paper has the following two limitations. First, there
is a lack of appropriate and fair judgment methods for the
results of reinforcement learning. This indicates that addi-
tional verification and evaluation are needed before applying
these reinforcement learning outcomes in real-world environ-
ments. To overcome this limitation, future research should
concentrate on developing and validating more sophisticated
equitable evaluation methods to enhance the reliability of the
research findings.
Second, there is no confirmation that the series of processes
for intelligent reconnaissance drones can be successfully ap-
plied and function in actual environments. In particular, the
critical aspect of communication, which may present real
challenges, is missing. Therefore, the sim-to-real aspect re-
mains a future task to be explored through procedures like
those shown in Figure 13. This paper represents Step 1,
where experiments are conducted within a simulator. As a
future research task for Step 2, the reinforcement learning
neural network will be implemented on a mission computer
to test its interoperability with the simulator. In Step 3, real
environments and sensors will be used, along with connecting
a flight controller for operation and enabling the monitoring
of drone information through the Ground Control System
(GCS). Through this process, it will be possible to refine
the procedures for intelligent reconnaissance drones to ensure
their suitability for actual environments.
FIGURE 13. Future research direction.
LIST OF ABBREVIATIONS
Abbreviation
Term
CNN
Convolutional Neural Network.
DEM
Digital Elevation Model.
EO
Electro-Optics.
GAN
Generative Adversarial Network.
GCS
Ground Control System.
GNSS
Global Navigation Satellite System.
IMU
Inertial Measurement Unit.
IR
Infra-Red.
LiDAR
Light Detection and Ranging.
LRF
Laser Range Finder.
PCA
Principal Component Analysis.
PID
Proportional-Integral-Derivative.
PPO
Proximal Policy Optimization.
RADAR
RAdio Detection And Ranging.
SAR
Synthetic Aperture Radar.
TEL
Transporter Erector Launcher.
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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
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