| Popis: |
Inspired by hummingbirds and certain insects, flapping wing micro aerial vehicles (FWMAVs) exhibit potential energy efficiency and maneuverability advantages. Among them, the bi-directional motor-driven tailless FWMAV with simple structure prevails in research, but it requires active pose control for hovering. In this paper, we employ deep reinforcement learning to train a low-level hovering strategy that directly maps the drone’s state to motor voltage outputs. To our knowledge, other FWMAVs in both reality and simulations still rely on classical proportional-derivative controllers for pose control. Our learning-based approach enhances strategy robustness through domain randomization, eliminating the need for manually fine-tuning gain parameters. The effectiveness of the strategy is validated in a high-fidelity simulation environment, showing that for an FWMAV with a wingspan of approximately 200 mm, the center of mass is maintained within a 20 mm radius during hovering. Furthermore, the strategy is utilized to demonstrate point-to-point flight, trajectory tracking, and controlled flight of multiple drones. |
