• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.31-37
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• 2019

In this paper, we propose an approach to get the requirement of drone acquisition for the efficient dronebot combat system using brute force algorithm. We define parameters, such as width, depth, and important surveillance area for the surveillance mission in the Army battalion and company units based on real military operation environment and brute force algorithm with 4 steps including first, next, valid, output is applied to get the requirement of drone acquisition and each drone's path planning using computer simulation. As a result, we could get the requirement of drone acquisition and each drone's path planning, the Army could utilize our proposed approach in the Army dronebot combat system. In the future research, we will study on the reliability of our proposed approach to get the requirement of drone acquisition for the efficient dronebot combat system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1187-1192
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• 2020

The dronebot combat system is a representative model of the future battlefield in the 4th industrial revolution. In dronebot, unmanned reconnaissance tankrobot can minimize human damage and reduce cost with higher combat power than humans. However, since the battlefield environment is very complex such as obstacles and enemy situations, it is also necessary for the pilot to control the tankrobot. Tankrobot are robots with new ICT technology, capable of hacking attacks, and if there is an abnormality in control, it can pose a threat to manipulation and control. A Bluetooth sniffing attack was performed on the communication section of the tankrobot and the controller to introduce a vulnerability to Bluetooth, and a countermeasure using MAC address exposure prevention and communication section encryption was proposed as a security measure. This paper first presented the vulnerability of tankrobot to be operated in future military operations, and will be the basic data that can be used for defense dronebot units.

• Journal of the Korea Society for Simulation
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• v.29 no.1
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• pp.47-55
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• 2020

This paper presents a real-time autonomous computation of shot numbers and aiming points against multiple soft targets on grounds by applying an unsupervised learning, k-mean clustering and Monte carlo simulation. For this computation, a 100 × 200 square meters size of virtual battlefield is created where an augmented enemy infantry platoon unit attacks, defences, and is scatted, and a virtual weapon with a lethal range of 15m is modeled. In order to determine damage types of the enemy unit: no damage, light wound, heavy wound and death, Monte carlo simulation is performed to apply the Carlton damage function for the damage effect of the soft targets. In addition, in order to achieve the damage effectiveness of the enemy units in line with the commander's intention, the optimal shot numbers and aiming point locations are calculated in less than 0.4 seconds by applying the k-mean clustering and repetitive Monte carlo simulation. It is hoped that this study will help to develop a system that reduces the decision time for 'detection-decision-shoot' process in battalion-scaled combat units operating Dronebot combat system.