System Development & Application
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 11
3363-3367

Research on solid radioactive waste grasping method based on deep reinforcement learning

Zhou Qijie1
Liu Manlu1,2
Li Xinmao1
Zhang Hua1
1. School of Information Engineering, Southwest University of Science & Technology, Mianyang Sichuan 621000, China
2. School of Information Science & Technology, University of Science & Technology of China, Hefei 230026, China
DOI: 10.19734/j.issn.1001-3695.2019.07.0288

Abstract

In the solid radioactive waste sorting operation, in order to solve the typical problems of disordered radioactive waste, low efficiency of remote teleoperation, and high risk of manual sorting, this paper proposed a method for grasping radioactive solid waste based on deep reinforcement learning. This method used an improved depth Q network algorithm. Through the acquired image information, the robot could continuously interact with the environment and obtain rewards, which were composed of the execution results of the robotic arm actions and the size of the radioactive activity in the radioactive area. According to the size of the Q value, the robot arm got the optimal grasping position. V-REP software established the simulation model of the UR5 robot arm, and different types of solid radioactive waste grasp training and testing were completed in the simulation environment. The simulation results show that when the radioactive solid waste is loosely placed, the method can make the grasping success rate greater than 90%, and the grasping success rate is more than 65% when the radioactive solid waste is placed tightly. The robotic arm grasping operation is not affected by the stacking of objects and the objects with high radioactivity in the radioactive area are preferentially grasped.

Foundation Support

国家“十三五”核能开发项目(20161295)
西南科技大学研究生创新基金资助项目(19ycx0103)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.07.0288
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 11
Section: System Development & Application
Pages: 3363-3367
Serial Number: 1001-3695(2020)11-033-3363-05

Publish History

[2020-11-05] Printed Article

Cite This Article

周祺杰, 刘满禄, 李新茂, 等. 基于深度强化学习的固体放射性废物抓取方法研究 [J]. 计算机应用研究, 2020, 37 (11): 3363-3367. (Zhou Qijie, Liu Manlu, Li Xinmao, et al. Research on solid radioactive waste grasping method based on deep reinforcement learning [J]. Application Research of Computers, 2020, 37 (11): 3363-3367. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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