An Efficient Deep Reinforcement Learning Framework for UAVs

Shanglin Zhou, Bingbing Li, Caiwu Ding, Lu Lu, Caiwen Ding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

3D Dynamic simulator such as Gazebo has become a popular substitution for unmanned aerial vehicle (UAV) because of its user-friendly in real-world scenarios. At this point, well-functioning algorithms on the UAV controller are needed for guidance, navigation, and control for autonomous navigation. Deep reinforcement learning (DRL) comes into sight as its famous self-learning characteristic. This goal-orientated algorithm can learn how to attain a complex objective or maximize along a particular dimension over many steps. In this paper, we propose a general framework to incorporate DRL with the UAV simulation environment. The whole system consists of the DRL algorithm for attitude control, packing algorithm on the Robot Operation System (ROS) to connect DRL with PX4 controller, and a Gazebo simulator that emulates the real-world environment. Experimental results demonstrate the effectiveness of the proposed framework.

Original languageEnglish (US)
Title of host publicationProceedings of the 21st International Symposium on Quality Electronic Design, ISQED 2020
PublisherIEEE Computer Society
Pages323-328
Number of pages6
ISBN (Electronic)9781728142074
DOIs
StatePublished - Mar 2020
Event21st International Symposium on Quality Electronic Design, ISQED 2020 - Santa Clara, United States
Duration: Mar 25 2020Mar 26 2020

Publication series

NameProceedings - International Symposium on Quality Electronic Design, ISQED
Volume2020-March
ISSN (Print)1948-3287
ISSN (Electronic)1948-3295

Conference

Conference21st International Symposium on Quality Electronic Design, ISQED 2020
Country/TerritoryUnited States
CitySanta Clara
Period3/25/203/26/20

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

Keywords

  • Deep Reinforcement Learning
  • Gazebo
  • PX4
  • Simulation Environment
  • Unmanned Aerial Vehicle

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