[1]南文虎,徐付民,叶伯生.记忆推理的放射源抓取机器人运动规划[J].深圳大学学报理工版,2022,39(3):343-348.[doi:10.3724/SP.J.1249.2022.03343]
 NAN Wenhu,XU Fumin,and YE Bosheng.Motion planning of radioactive source grasping robot based on memory reasoning[J].Journal of Shenzhen University Science and Engineering,2022,39(3):343-348.[doi:10.3724/SP.J.1249.2022.03343]
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记忆推理的放射源抓取机器人运动规划()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第39卷
期数:
2022年第3期
页码:
343-348
栏目:
电子与信息科学
出版日期:
2022-05-16

文章信息/Info

Title:
Motion planning of radioactive source grasping robot based on memory reasoning
文章编号:
202203013
作者:
南文虎1 徐付民1 叶伯生2
1)兰州理工大学机电工程学院,甘肃兰州730050
2)华中科技大学国家数控工程中心,湖北武汉 430074
Author(s):
NAN Wenhu1 XU Fumin1 and YE Bosheng2
1) School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, Gansu Province, P. R. China
2) The National CNC Engineering Center, Huazhong University of Science and Technology, Wuhan 430074, Hubei Province, P. R. China
关键词:
智能机器人 力觉反馈 历史抓取数据 记忆推理决策 自主抓取 Gazebo仿真器 放射源抓取
Keywords:
intelligent robot force feedback historical grasping data memory reasoning decision autonomous grasping Gazebo simulator radioactive sources grasping
分类号:
TP24
DOI:
10.3724/SP.J.1249.2022.03343
文献标志码:
A
摘要:
针对机器人抓取铅罐内部放射源时,因铅罐的半封闭和强辐射环境导致机器视觉难以应用于放射源抓取的问题,提出一种记忆推理的强化学习抓取方法.基于机器视觉构建智能机器人抓取系统运动学模型,采用力觉反馈实现智能机器人与铅罐内部环境的交互,通过对历史抓取数据的记忆推理决策,实现对放射源的自主抓取.在机器人操作系统中使用Gazebo仿真器,分别采用蒙特卡罗采样法和基于记忆推理的强化学习抓取方法进行仿真.结果表明,基于记忆推理的强化学习抓取方法的平均抓取效率比蒙特卡罗采样法高84.67%,能高效地解决铅罐内部放射源的自主抓取问题.
Abstract:
Aiming at the problem that machine vision is difficult to be applied to the radioactive source grasping due to the semi-closed and strong radiation environment of the lead can, we propose a memory reasoning based reinforcement learning grasping method. The kinematics model of intelligent robot grasping system is constructed based on machine vision. The interaction between the intelligent robot and internal environment of lead cans is realized by force feedback. Through the memory reasoning decision of historical grasping data, the autonomous grasping of radioactive sources is realized. Using the Gazebo simulator in robot operating system (ROS), the Monte Carlo sampling method and reinforcement learning grasping method based on memory reasoning are simulated, respectively. The results show that the reinforcement learning grasping method based on memory reasoning achieves the average grasping efficiency of 84.67% higher than that of Monte Carlo sampling method and thus demonstrate that the reinforcement learning grasping method can effectively solve the problem of autonomous grasping of radioactive sources in lead cans.

参考文献/References:

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备注/Memo

备注/Memo:
Received: 2021-03-06; Accepted: 2021-07-02; Online (CNKI): 2022-01-13
Foundation: National Key R & D Program of China (2017YFB1301400); Natural Science Foundation of Gansu Province (17JR5RA111)
Corresponding author: Associate professor YE Bosheng. E-mail: 396207773@qq.com
Citation: NAN Wenhu, XU Fumin, YE Bosheng. Motion planning of radioactive source grasping robot based on memory reasoning [J]. Journal of Shenzhen University Science and Engineering, 2022, 39(3): 343-348.(in Chinese)
基金项目:国家重点研发计划资助项目(2017YFB1301400);甘肃省自然科学基金资助项目(17JR5RA111)
作者简介:南文虎(1985—),兰州理工大学副教授、博士.研究方向:工业机器人轨迹规划与控制技术.E-mail: nanwenhu@163.com
引 文:引用格式:南文虎,徐付民,叶伯生.记忆推理的放射源抓取机器人运动规划[J].深圳大学学报理工版,2022,39(3):343-348.
更新日期/Last Update: 2022-05-30