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Alternative TitleDeep Reinforcement Learning Based Robotic Assembly in Simulation
刘乃龙1,2; 刘钊铭1,2; 崔龙1
Source Publication计算机仿真
Contribution Rank1
Funding Organization国家自然科学基金资助项目( 91648201)
Keyword机器人仿真 机器人装配 深度强化学习 人工智能 三维图形仿真


Other Abstract

Robotic peg - in - hole task is a key problem in robotic assembly tasks,and many robotic assembly manipulation tasks can be treated as the peg - in - hole tasks in manufacturing industry. The traditional peg - in - hole methods depend on the fixed and structured environment,and can always be applied in some specific geometric products,which may cause the task failed if the environment was changed and destroy the products or lose the robot precision when the robot end - effector is subjected to a great force. In the smart phone manufacturing industry,products need personalized customizations,and this requires frequent changes in the production line. The traditional robot tasks often rely on a large number of manual programming and hard coding methods,and it is difficult to adapt to this rapid product line changes. In view of the limitations of the traditional method,this paper proposes a self - learning method of robotic peg - in - hole task based on deep reinforcement learning. This method uses deep neural network as the control policy of robot,and uses reinforcement learning method to train the deep neural network to improve the generalization ability of the robot control policy which can makes robot adapt to more uncertainty. This method does not require a lot of manual programming and hard coding strategy. The article also verified the validity of the method in a simulation environment based on ROS and Gazebo.

Document Type期刊论文
Corresponding Author刘乃龙
Recommended Citation
GB/T 7714
刘乃龙,刘钊铭,崔龙. 基于深度强化学习的仿真机器人轴孔装配研究[J]. 计算机仿真,2019,36(12):296-301.
APA 刘乃龙,刘钊铭,&崔龙.(2019).基于深度强化学习的仿真机器人轴孔装配研究.计算机仿真,36(12),296-301.
MLA 刘乃龙,et al."基于深度强化学习的仿真机器人轴孔装配研究".计算机仿真 36.12(2019):296-301.
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