한국군사과학기술학회지 (Journal of the Korea Institute of Military Science and Technology)

  • 제27권6호
  • /
  • Pages.772-783
  • /
  • 2024
  • /
  • 1598-9127(pISSN)
  • /
  • 2636-0640(eISSN)
한국군사과학기술학회 (The Korea Institute of Military Science and Technology)
권호 표지
DOI QR코드

DOI QR Code

유무인복합체계 구성 및 협업통제 자동화

Autonomous Collaboration Control for Manned-Unmanned Complex Systems and Its Compositions

  • 이호주 (국방과학연구소 국방AI센터) ;
  • 김도현 (국방과학연구소 국방AI센터) ;
  • 박원익 (국방과학연구소 국방AI센터) ;
  • 최준성 (국방과학연구소 국방AI센터)
  • Hojoo Lee (Defense AI Center, Agency for Defense Development) ;
  • Dohyun Kim (Defense AI Center, Agency for Defense Development) ;
  • Wonik Park (Defense AI Center, Agency for Defense Development) ;
  • Joonsung Choi (Defense AI Center, Agency for Defense Development)
  • 투고 : 2024.07.19
  • 심사 : 2024.10.10
  • 발행 : 2024.12.05
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The emergence of MUCS(Manned-Unmanned Complex System), incorporating numerous robots surpassing human's control capabilities, is inevitable on future battlefields and necessitates revolutionary robot operation technology. Since MUCS should be structured over the current command and control networks in Korean military binding its constituent elements ranging from small echelons to joint forces, various types of MUCS configurations and manned-unmanned teaming(MUM-T) types are also defined. Then a methodology for robot collaboration with aiming at real-time situation response is proposed. The method is basing on the situational response decision-making model in order to operate multiple robots cooperatively in respond to serial events occurring in real-time using the concept of control measure which is the origin/object triggering a task. In addition, a set of decision-making rules is devised and compared to decisions optimized by the model. Through illustrative experiments the suggested method is checked to be viable for realizing MUM-T and operating multiple robots in MUCSs.

키워드

과제정보

본 논문은 2024년 정부의 재원으로 수행된 연구 결과임. 논문에 대한 심사평 반영 차원에서 본 논문에 포함된 모든 그림은 저자의 주관에 따라 직접 그린 것임을 밝힙니다.

참고문헌

  1. W. I. Park, H. J. Lee and J. H. Lee, "Multi-swarming robots collaboration control methodology," Defense Science Technology Plus, Vol. 2018, No. 242, pp. 62-68, 2018.
  2. B. Fu, W. Smith, D. Rizzo, M. Castanier, M. Ghaffari, and K. Barton, "Learning Task Requirements and Agent Capabilities for Multi-agent Task Allocation," International Journal of Control, Automation, and Systems, Vol. 20, No. 2, pp. 567-584, 2022.
  3. B. Fu, T. Kathuria, "Simultaneous Human-Robot Matching and Routing for Multi-Robot Tour Guiding Under Time Uncertainty," Journal of Autonomous Vehicles and Systems, Vol. 15, No. 4, pp. 210-235, 2021.
  4. J. Guzzi, "Path Planning in Multi-Robot Systems: Efficient and Scalable Approaches," Autonomous Robots, Vol. 44, No. 5, pp. 665-682, 2020.
  5. D. Liu and G. Bekey, "Robust Multi-Robot Coordination with Fault Detection and Recovery," IEEE Robotics and Automation Letters, Vol. 5, No. 2, pp. 234-241, 2020.
  6. S. Bhattacharya, S. Kailas, S. Badyal, S. Gil, and D. Bertsekas, "Multiagent Rollout and Policy Iteration for POMDP with Application to Multi-Robot Repair Problems," in Proc. 4th Conference on Robot Learning, Vol. 1, No. 1, pp. 45-60, 2020.
  7. A. Viryasov and M. Hirche, "Coordination Strategies for Multi-Robot Systems under Communication Constraints," IEEE Transactions on Robotics, Vol. 37, No. 1, pp. 45-60, 2021.
  8. S. Kumar and R. Arkin, "Behavior-Based Control for Multi-Robot Systems: A Decentralized Approach," Robotics and Autonomous Systems, Vol. 102, No. 3, pp. 1-12, 2018.
  9. H. Cheng and Y. Nakamura, "Coordination Mechanisms for Multi-Robot Systems in Uncertain Environments," IEEE Transactions on Automation Science and Engineering, Vol. 20, No. 2, pp. 333-347, 2023.