基于深度强化学习的无人机自主感知−规划−控制策略

In recent years, with the rapid development of deep reinforcement learning (DRL) methods, their application in the field of unmanned aerial vehicle (UAV) autonomous navigation has attracted increasing attention. However, when facing complex and unknown environments, existing DRL-based UAV autonomous navigation algorithms are often limited by their dependence on global information and the constraints of specific training environments, greatly limiting their potential for application in various scenarios. To address these issues, multi-scale input is proposed to balance the receptive field and the state dimension, and truncation operation is proposed to enable the agent to operate in the expanded environment. In addition, the autonomous perception-planning-control architecture is constructed to give the UAV the ability to navigate autonomously in diverse and complex environments.