Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control

• Published in: arXiv.org
• Main Authors: Yun, Won Joon, Park, Soohyun, Kim, Joongheon, Shin, MyungJae, Jung, Soyi, Mohaisen, David A, Jae-Hyun, Kim
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 15.01.2022
• Subjects: Algorithms; Closed circuit television; Computer Science - Artificial Intelligence; Computer Science - Learning; Computer Science - Robotics; Computer Science - Systems and Control; Deep learning; Machine learning; Multiagent systems; Smart cities; Surveillance; Unmanned aerial vehicles; User services
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2201.05843

CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a key technology for security in smart city environments. This paper creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services. UAVs should be deployed to cover a large area while minimize overlapping and shadow areas for a reliable surveillance system. However, the operation of UAVs is subject to high uncertainty, necessitating autonomous recovery systems. This work develops a multi-agent deep reinforcement learning-based management scheme for reliable industry surveillance in smart city applications. The core idea this paper employs is autonomously replenishing the UAV's deficient network requirements with communications. Via intensive simulations, our proposed algorithm outperforms the state-of-the-art algorithms in terms of surveillance coverage, user support capability, and computational costs.