Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

Robot Motion Planning Under Uncertain Condition Using Deep Reinforcement Learning

Authors
Zhuang Chen, Lin Zhou, Min Guo
Corresponding Author
Zhuang Chen
Available Online March 2018.
DOI
https://doi.org/10.2991/mecae-18.2018.22How to use a DOI?
Keywords
Motion planning, Reinforcement learning, Deep reinforcement learning, Uncertain condition.
Abstract
The motion planning of industrial robot plays an important role in today's production systems, such as Made in China 2025 and Industry 4.0. The motion planning under uncertain condition is an important research topic in autonomous robots. For promoting the ability of motion planning to adapt to the environment change, in this paper, we propose a deep reinforcement learning (DRL) method which combines reinforcement learning with deep learning for industrial robot motion planning. Our work shows that the DRL-agent is capable of learning how to control the robot to successfully reach robotic tasks without explicit prior Knowledge of kinematics. We conclude that DRL has great potential for industrial robots and production systems, especially in robot motion planning.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
Part of series
Advances in Engineering Research
Publication Date
March 2018
ISBN
978-94-6252-493-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/mecae-18.2018.22How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zhuang Chen
AU  - Lin Zhou
AU  - Min Guo
PY  - 2018/03
DA  - 2018/03
TI  - Robot Motion Planning Under Uncertain Condition Using Deep Reinforcement Learning
BT  - 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-18.2018.22
DO  - https://doi.org/10.2991/mecae-18.2018.22
ID  - Chen2018/03
ER  -