Autonomous flying of drone based on ppo reinforcement learning algorithm

Sung Gwan Park, Dong Hwan Kim

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

In this study, the performance of autonomous flight was analyzed by introducing the PPO method as reinforcement learning for autonomous flight of drones. A simulator based on the dynamics of a drone was produced, and the performance of autonomous flight was confirmed when reinforcement learning was applied to a drone using this simulator. After that, the possibility of autonomous flight was confirmed by applying the PPO algorithm to the actual drone. Also, a lightweight embedded PC was attached to the drone to perform independent calculations to simultaneously construct obstacle avoidance and path planning.

Original languageEnglish
Pages (from-to)955-963
Number of pages9
JournalJournal of Institute of Control, Robotics and Systems
Volume26
Issue number11
DOIs
StatePublished - Nov 2020

Keywords

  • Autonomous drone
  • PPO(Proximal Policy Optimization)
  • Reinforcement learning
  • Simulator

Fingerprint

Dive into the research topics of 'Autonomous flying of drone based on ppo reinforcement learning algorithm'. Together they form a unique fingerprint.

Cite this