지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.6
- 수록면
- 587 - 595 (9page)
- DOI
- 10.5302/J.ICROS.2024.24.0009
이용수
초록· 키워드
오류제보하기
In this study, a cooperative monitoring task for multiple anti-aircraft targets is investigated using multiple unmanned aerial vehicles (UAVs) equipped with stereo vision sensors. To accomplish this task, these UAVs must cooperate with other UAVs within a three-dimensional (3D) space to track multiple targets and avoid collisions. To address this challenge, we propose a cooperative multiagent reinforcement learning (MARL) scheme that can make intelligent flight decisions to enable multiple UAVs to perform cooperative surveillance tasks. The main contributions of this study are as follows. First, to the best of our knowledge, this study is the first attempt to address aerial multi-target surveillance using multiple UAVs via MARL in 3D space. Second, we adopt a hybrid approach that integrates low-level rule-based controllers and high-level control policies to enable agents to learn the high-level goals set through reinforcement learning.
목차
Abstract
I. 서론
II. UAV 제어 정책
III. 이동 객체 인지
IV. 다중 객체 추적
V. 다중 UAV 의사 결정
VI. 제어 정책 실험 결과
VII. 다중 대공 표적 감시 실험 결과
VIII. 결론
REFERENCES
참고문헌 (18)
참고문헌 신청
X. Shi, C. Yang, W. Xie, C. Liang, Z. Shi, and J. Chen, “Anti-drone system with multiple surveillance technologies: architecture, implementation, and challenges,” IEEE Communications Magazine, vol. 56, no. 4, pp. 68-74, April. 2018. doi: http://dx.doi.org/10.1109/MCOM.2018.1700430
J. Gu, T. Su, Q. Wang, X. Du, and M. Guizani, “Multiple moving targets surveillance based on a cooperative network for multi-UAV,” IEEE Communications Magazine, vol. 56, no. 4, pp. 82-89, April. 2018. doi: http://dx.doi.org/10.1109/MCOM.2018.1700422
Y.-J. Chen, D.-K. Chang, and C. Zhang, “Autonomous tracking using a swarm of UAVs: A constrained multi-agent reinforcement learning approach,” IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 13702-13717, September. 2020. doi: http://dx.doi.org/10.1109/TVT.2020.3023733
W. Zhou, Z. Liu, J. Li, X. Xu, and L. Shen, “Multi-target tracking for unmanned aerial vehicle swarms using deep reinforcement learning,” Neurocomputing, vol. 466, pp. 285-297, November. 2021. doi: https://doi.org/10.1016/j.neucom.2021.09.044
W. J. Yun, S. Park, J. Kim, M. Shin, S. Jung, D. A. Mohaisen, and J.-H. Kim, “Cooperative multiagent deep reinforcement learning for reliable surveillance via autonomous multi-UAV control,” IEEE Transactions on Industrial Informatics, vol. 18, no. 10, pp. 7086-7096, January. 2022. doi: https://doi.org/10.1109/TII.2022.3143175
J. Gu, T. Su, Q. Wang, X. Du, and M. Guizani, “Multiple moving targets surveillance based on a cooperative network for multi-UAV,” IEEE Communications Magazine, vol. 56, no. 4, pp. 82-89, April. 2018. doi: http://dx.doi.org/10.1109/MCOM.2018.1700422
Y.-J. Chen, D.-K. Chang, and C. Zhang, “Autonomous tracking using a swarm of UAVs: A constrained multi-agent reinforcement learning approach,” IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 13702-13717, September. 2020. doi: http://dx.doi.org/10.1109/TVT.2020.3023733
W. Zhou, Z. Liu, J. Li, X. Xu, and L. Shen, “Multi-target tracking for unmanned aerial vehicle swarms using deep reinforcement learning,” Neurocomputing, vol. 466, pp. 285-297, November. 2021. doi: https://doi.org/10.1016/j.neucom.2021.09.044
W. J. Yun, S. Park, J. Kim, M. Shin, S. Jung, D. A. Mohaisen, and J.-H. Kim, “Cooperative multiagent deep reinforcement learning for reliable surveillance via autonomous multi-UAV control,” IEEE Transactions on Industrial Informatics, vol. 18, no. 10, pp. 7086-7096, January. 2022. doi: https://doi.org/10.1109/TII.2022.3143175
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