A Model-free Deep Reinforcement Learning Approach To Maneuver A Quadrotor Despite Single Rotor Failure
This addresses fault recovery for quadrotors in applications like drones, though it is incremental as it builds on existing reinforcement learning methods.
The paper tackled the problem of enabling a quadrotor to recover from a single rotor failure using a model-free deep reinforcement learning approach, achieving hover, landing, and path following in 2D and 3D simulations with robustness to wind disturbances.
Ability to recover from faults and continue mission is desirable for many quadrotor applications. The quadrotor's rotor may fail while performing a mission and it is essential to develop recovery strategies so that the vehicle is not damaged. In this paper, we develop a model-free deep reinforcement learning approach for a quadrotor to recover from a single rotor failure. The approach is based on Soft-actor-critic that enables the vehicle to hover, land, and perform complex maneuvers. Simulation results are presented to validate the proposed approach using a custom simulator. The results show that the proposed approach achieves hover, landing, and path following in 2D and 3D. We also show that the proposed approach is robust to wind disturbances.