Autonomous flying of drone based on ppo reinforcement learning algorithm

被引：5

作者：

Park S.G. ^{[1
]}

Kim D.H. ^{[2
]}

机构：

[1] Dept. of Mechanical Design and Robot Engineering, Seoul National University of Science and Technology

[2] Dept. of Mechanical System Design Engineering, Seoul National University of Science and Technology

来源：

Kim, Dong Hwan (dhkim@seoultech.ac.kr) | 1600年 / Institute of Control, Robotics and Systems卷 / 26期

关键词：

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

D O I：

10.5302/J.ICROS.2020.20.0125

中图分类号：

学科分类号：

摘要：

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. Copyright© ICROS 2020.

引用

页码：955 / 963

页数：8

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