Inclined Quadrotor Landing using Deep Reinforcement Learning

Abstract

Landing a quadrotor on an inclined surface is a challenging maneuver. The final state of any inclined landing trajectory is not an equilibrium, which precludes the use of most conventional control methods. We propose a deep reinforcement learning approach to design an autonomous landing controller for inclined surfaces. Using the proximal policy optimization (PPO) algorithm with sparse rewards and a tailored curriculum learning approach, an inclined landing policy can be trained in simulation in less than 90 minutes on a standard laptop. The policy then directly runs on a real Crazyflie 2.1 quadrotor and successfully performs real inclined landings in a flying arena. A single policy evaluation takes approximately 2.5\,ms, which makes it suitable for a future embedded implementation on the quadrotor.

arXiv comment8 pages, 4 figures. Published in IROS 2021

References in corpus (6)

• Continuous control with deep reinforcement learning
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
• Addressing Function Approximation Error in Actor-Critic Methods
• OpenAI Gym
• Control of a Quadrotor with Reinforcement Learning
• Neural Lander: Stable Drone Landing Control using Learned Dynamics

Cited by in corpus (2)

• Using Simulation Optimization to Improve Zero-shot Policy Transfer of Quadrotors
• Swooper: Learning High-Speed Aerial Grasping With a Simple Gripper