UAV Autonomous Navigation Based on Multi-modal Perception: A Deep Hierarchical Reinforcement Learning Method

Autonomous navigation is a highly desirable capability for Unmanned Aerial Vehicle (UAV). In this paper, the problem of autonomous navigation of UAV in unknown dynamic environments is addressed. Specifically, we propose a visual-inertial multi-modal sensor data perception framework based on a hierarchical reinforcement learning paradigm. This model consists of a high-level behavior selection model and a low-level policy execution model. The high-level model learns a stable and reliable behavior selection strategy. The low-level model decomposes the UAV navigation task into two simpler subtasks, which respectively achieve obstacle avoidance and goal approximation, which effectively learns high-level semantic information about the scene and narrows the strategy space. Furthermore, extensive simulation results are provided to confirm the superiority of the proposed method in terms of convergence and effectiveness compared to state-of-the-art methods.