International Journal of Computational Intelligence Systems

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Volume 13, Issue 1, 2020, Pages 914 - 919

Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning

Authors
Che-Cheng Chang1, Jichiang Tsai2, *, Peng-Chen Lu3, Chuan-An Lai1
1Department of Information Engineering and Computer Science, Feng Chia University, No. 100, Wenhua Rd., Xitun Dist., Taichung City 407, Taiwan (R.O.C.)
2Department of Electrical Engineering and Graduate Institute of Communication Engineering, National Chung Hsing University, No. 145, Xingda Rd., South Dist., Taichung City 402, Taiwan (R.O.C.)
3Graduate Institute of Communication Engineering, National Chung Hsing University, No. 145, Xingda Rd., South Dist., Taichung City 402, Taiwan (R.O.C.)
*Corresponding author. Email: jichiangt@nchu.edu.tw
Corresponding Author
Jichiang Tsai
Received 31 January 2020, Accepted 9 June 2020, Available Online 25 June 2020.
DOI
10.2991/ijcis.d.200615.002How to use a DOI?
Keywords
Drones; Deep reinforcement learning; Q-learning; Autonomous flight
Abstract

Nowadays, drones are expected to be used in several engineering and safety applications both indoors and outdoors, e.g., exploration, rescue, sport, entertainment, and convenience. Among those applications, it is important to make a drone capable of flying autonomously to carry out an inspection patrol. In this paper, we present a novel method that uses ArUco markers as a reference to improve the accuracy of a drone on autonomous straight take-off, flying forward, and landing based on Deep Reinforcement Learning (DRL). More specifically, the drone first detects a specific marker with one of its onboard cameras. Then it calculates the position and orientation relative to the marker so as to adjust its actions for achieving better accuracy with a DRL method. We perform several simulation experiments with different settings, i.e., different sets of states, different sets of actions and even different DRL methods, by using the Robot Operating System (ROS) and its Gazebo simulator. Simulation results show that our proposed methods can efficiently improve the accuracy of the considered actions.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
914 - 919
Publication Date
2020/06/25
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.200615.002How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Che-Cheng Chang
AU  - Jichiang Tsai
AU  - Peng-Chen Lu
AU  - Chuan-An Lai
PY  - 2020
DA  - 2020/06/25
TI  - Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning
JO  - International Journal of Computational Intelligence Systems
SP  - 914
EP  - 919
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200615.002
DO  - 10.2991/ijcis.d.200615.002
ID  - Chang2020
ER  -