Motion planning by reinforcement learning for an unmanned aerial vehicle in virtual open space with static obstacles

Sanghyun Kim; Jongmin Park; Jae Kwan Yun; Jiwon Seo

doi:10.23919/ICCAS50221.2020.9268253

Motion planning by reinforcement learning for an unmanned aerial vehicle in virtual open space with static obstacles

Sanghyun Kim, Jongmin Park, Jae Kwan Yun, Jiwon Seo

School of Integrated Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this study, we applied reinforcement learning based on the proximal policy optimization algorithm to perform motion planning for an unmanned aerial vehicle (UAV) in an open space with static obstacles. The application of reinforcement learning through a real UAV has several limitations such as time and cost; thus, we used the Gazebo simulator to train a virtual quadrotor UAV in a virtual environment. As the reinforcement learning progressed, the mean reward and goal rate of the model were increased. Furthermore, the test of the trained model shows that the UAV reaches the goal with an 81% goal rate using the simple reward function suggested in this work.

Original language	English
Title of host publication	2020 20th International Conference on Control, Automation and Systems, ICCAS 2020
Publisher	IEEE Computer Society
Pages	784-787
Number of pages	4
ISBN (Electronic)	9788993215205
DOIs	https://doi.org/10.23919/ICCAS50221.2020.9268253
Publication status	Published - 2020 Oct 13
Event	20th International Conference on Control, Automation and Systems, ICCAS 2020 - Busan, Korea, Republic of Duration: 2020 Oct 13 → 2020 Oct 16

Publication series

Name	International Conference on Control, Automation and Systems
Volume	2020-October
ISSN (Print)	1598-7833

Conference

Conference	20th International Conference on Control, Automation and Systems, ICCAS 2020
Country	Korea, Republic of
City	Busan
Period	20/10/13 → 20/10/16

Bibliographical note

Funding Information:
This work was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government [20ZR1100, Core Technologies of Distributed Intelligence Things for Solving Industry and Society Problems].

Publisher Copyright:
© 2020 Institute of Control, Robotics, and Systems - ICROS.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications
Control and Systems Engineering
Electrical and Electronic Engineering

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10.23919/ICCAS50221.2020.9268253

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Kim, S., Park, J., Yun, J. K., & Seo, J. (2020). Motion planning by reinforcement learning for an unmanned aerial vehicle in virtual open space with static obstacles. In 2020 20th International Conference on Control, Automation and Systems, ICCAS 2020 (pp. 784-787). [9268253] (International Conference on Control, Automation and Systems; Vol. 2020-October). IEEE Computer Society. https://doi.org/10.23919/ICCAS50221.2020.9268253

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title = "Motion planning by reinforcement learning for an unmanned aerial vehicle in virtual open space with static obstacles",

abstract = "In this study, we applied reinforcement learning based on the proximal policy optimization algorithm to perform motion planning for an unmanned aerial vehicle (UAV) in an open space with static obstacles. The application of reinforcement learning through a real UAV has several limitations such as time and cost; thus, we used the Gazebo simulator to train a virtual quadrotor UAV in a virtual environment. As the reinforcement learning progressed, the mean reward and goal rate of the model were increased. Furthermore, the test of the trained model shows that the UAV reaches the goal with an 81% goal rate using the simple reward function suggested in this work.",

author = "Sanghyun Kim and Jongmin Park and Yun, {Jae Kwan} and Jiwon Seo",

note = "Funding Information: This work was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government [20ZR1100, Core Technologies of Distributed Intelligence Things for Solving Industry and Society Problems]. Publisher Copyright: {\textcopyright} 2020 Institute of Control, Robotics, and Systems - ICROS. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 20th International Conference on Control, Automation and Systems, ICCAS 2020 ; Conference date: 13-10-2020 Through 16-10-2020",

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doi = "10.23919/ICCAS50221.2020.9268253",

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Kim, S, Park, J, Yun, JK & Seo, J 2020, Motion planning by reinforcement learning for an unmanned aerial vehicle in virtual open space with static obstacles. in 2020 20th International Conference on Control, Automation and Systems, ICCAS 2020., 9268253, International Conference on Control, Automation and Systems, vol. 2020-October, IEEE Computer Society, pp. 784-787, 20th International Conference on Control, Automation and Systems, ICCAS 2020, Busan, Korea, Republic of, 20/10/13. https://doi.org/10.23919/ICCAS50221.2020.9268253

Motion planning by reinforcement learning for an unmanned aerial vehicle in virtual open space with static obstacles. / Kim, Sanghyun; Park, Jongmin; Yun, Jae Kwan; Seo, Jiwon.

2020 20th International Conference on Control, Automation and Systems, ICCAS 2020. IEEE Computer Society, 2020. p. 784-787 9268253 (International Conference on Control, Automation and Systems; Vol. 2020-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - Funding Information: This work was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government [20ZR1100, Core Technologies of Distributed Intelligence Things for Solving Industry and Society Problems]. Publisher Copyright: © 2020 Institute of Control, Robotics, and Systems - ICROS. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

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Kim S, Park J, Yun JK, Seo J. Motion planning by reinforcement learning for an unmanned aerial vehicle in virtual open space with static obstacles. In 2020 20th International Conference on Control, Automation and Systems, ICCAS 2020. IEEE Computer Society. 2020. p. 784-787. 9268253. (International Conference on Control, Automation and Systems). https://doi.org/10.23919/ICCAS50221.2020.9268253

Motion planning by reinforcement learning for an unmanned aerial vehicle in virtual open space with static obstacles

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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