A_Study_on_the_Advancement_of_Intelligent_Military.pdf — page 1

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A Study on the Advancement of Intelligent
Military Drones: Focusing on Reconnaissance
Operations
MINHO LEE1, MINWOO CHOI1, TAEHOON YANG2, JINGU KIM3, JAEOH KIM2, OJEONG KWON4,
NAMSUK CHO1
1Department of Operation Research, Korea National Defense University, Nonsan 33021, South Korea
2Department of Data Science, Inha University, Incheon 22212, South Korea
3Defence R&D Institute, Poongsan, Daejeon 34027, South Korea
4Department of Industrial Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea
Corresponding author: Namsuk Cho (e-mail: ncho64@gmail.com)
This research was conducted with the support of the Poongsan-KAIST Future Technology Research Center
ABSTRACT Drones have become integral in modern warfare, and their evolution towards greater autonomy
is inevitable. This study explores the trajectory towards intelligent, minimally human-dependent military
drones, detailing the necessary technological advancements. We simulate drone reconnaissance operations to
identify and analyze emergent challenges. This study delves into various technologies critical for enhancing
drone intelligence, with a focus on reinforcement learning based on object detection, and presents practical
implementation examples to illustrate these advancements. Our findings confirm the significant potential for
enhancing the intelligence of military drones, paving the way for more autonomous and effective combat
solutions.
INDEX TERMS
Intelligent military drones, reconnaissance drone, reinforcement learning, autonomy,
simulation, object detection.
I. INTRODUCTION
D
RONES have been recognized as essential forces
through various proven instances in recent conflicts
such as the Russian Invasion of Ukraine and the Armenia-
Azerbaijan War. Currently, the majority of combat-ready
drones are remotely controlled. Although there is some level
of automation in drones, operators are still required due to
technical and ethical issues. From a tactical perspective, the
best advantages of drones are “low cost” and “mass deploy-
ment.” However, these two advantages only work when the
drone can be controlled without operators, that is, when the
drone is intelligent.
Autonomous drones alone are not a novel concept, as
extensive research has already been conducted. For instance,
we live in an era where unmanned drones are used for deliv-
eries and search and rescue missions [1], [2], [3]. However,
can civilian intelligent drone technology be used directly for
military purposes? Our answer is “no,” because military drone
operations show clear differences from civilian drones in the
following situations. First, the environment in the military
is more complex than that in the civilian sector. Consider
when Tesla is on Autopilot on an unpaved road how often
a driver must intervene. Military operations do not occur on
‘‘well-paved roads.’’ Moreover, military operations involve
missions assigned in arbitrary locations. Second, countless
enemy counteractions accompany military operations. These
counteractions include both active and passive denial, with
active denial involving interception attempts and passive de-
nial including concealment and deception. Such enemy ac-
tivities increase the complexity of the problem. Third, due to
the military’s uniqueness and security, there is a lack of data
related to military operations. For example, there is a shortage
of aerial view data of tanks and Transporter Erector Launchers
(TELs), which are used targets of object detection. Fourth,
safety and ethical aspects need to be considered when a mili-
tary intelligent drone performs a mission. An intelligent drone
lacking stability during operations can result in unpredictable
behaviors, leading to indiscriminate casualties and mission
failure. As an ethical consideration, even though overall drone
operation becomes intelligent, there is a need for the concept
that the final attack decision is made by humans. In other
words, the key consideration should not be whether drones
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