A Model-free Deep Reinforcement Learning Approach To Maneuver A Quadrotor Despite Single Rotor Failure

TL;DR

This paper presents a model-free deep reinforcement learning method enabling a quadrotor to recover from single rotor failure, perform complex maneuvers, and maintain robustness against disturbances, validated through simulation.

Contribution

It introduces a novel DRL approach using Soft-actor-critic for fault recovery in quadrotors without relying on a model, demonstrating effectiveness in simulation.

Findings

Successfully achieves hover, land, and path following in simulation.

Demonstrates robustness to wind disturbances.

Validates approach with a custom simulator.

Abstract

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.