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SAC (Soft Actor Critic) 알고리즘을 이용한 무인항공기 경로 계획
현수종(Soo-Jong Hyeon),강태영(Tae Young Kang),유창경(Chang-Kyung Ryoo) 제어로봇시스템학회 2022 제어·로봇·시스템학회 논문지 Vol.28 No.2
Path planning is an essential element in the autonomous flight control of unmanned aerial vehicles, where it is important to quickly establish the path in uncertain environments and avoid collisions with the terrain and obstacles. In particular, research and development of fully autonomous flight is necessary in the case of unmanned aerial vehicles performing search, reconnaissance, and detection in terrain where human intervention is difficult. This paper proposes a path planning design method using machine learning. It has the advantages of fast calculation speed and high repeatability in a two-dimensional environment. Using the Soft Actor–Critic (SAC), an algorithm based on reinforcement learning, research into machine learning, observation status, behavior, and reward functions are required to generate global paths. Additionally, the learning and path generation results are analyzed by conducting a learning-based path planning simulation in an environment with dynamic obstacles.