A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform

TL;DR

This paper presents a novel deep reinforcement learning framework using Gazebo simulation for autonomous drone landing, demonstrating successful task completion and bridging the gap between simulation and real-world application.

Contribution

It introduces a ROS-RL framework integrating reinforcement learning algorithms with Gazebo for drone control, enabling effective autonomous landing in simulation.

Findings

Reinforcement learning algorithms successfully achieved autonomous drone landing.

The proposed framework effectively bridges simulation and real-world drone control.

Experimental results confirm the effectiveness of the RL-based landing strategy.

Abstract

With the development of industry, drones are appearing in various field. In recent years, deep reinforcement learning has made impressive gains in games, and we are committed to applying deep reinforcement learning algorithms to the field of robotics, moving reinforcement learning algorithms from game scenarios to real-world application scenarios. We are inspired by the LunarLander of OpenAI Gym, we decided to make a bold attempt in the field of reinforcement learning to control drones. At present, there is still a lack of work applying reinforcement learning algorithms to robot control, the physical simulation platform related to robot control is only suitable for the verification of classical algorithms, and is not suitable for accessing reinforcement learning algorithms for the training. In this paper, we will face this problem, bridging the gap between physical simulation platforms and intelligent agent, connecting intelligent agents to a physical simulation platform, allowing agents to learn and complete drone flight tasks in a simulator that approximates the real world. We proposed a reinforcement learning framework based on Gazebo that is a kind of physical simulation platform (ROS-RL), and used three continuous action space reinforcement learning algorithms in the framework to dealing with the problem of autonomous landing of drones. Experiments show the effectiveness of the algorithm, the task of autonomous landing of drones based on reinforcement learning achieved full success.