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面向数字孪生的机械臂抓取系统碰撞检测方法的研究
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中央引导地方科技发展专项资金项目(YDZJSX2022A032);山西重点研发计划项目(201903D421006);中北大学研究生科技立项(20221815)


Research on Collision Detection Method of Grasping System for Digital Twin Manipulator
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    摘要:

    机械臂运行过程中,机械臂之间及机械臂与外部环境中的人和物易发生碰撞,造成财产损失或人员受伤甚至死亡。针对此,在对比分析目前主流的路径规划算法后,将卷积神经网络与RRT Connect算法进行融合,弥补了原始算法随机性强、算法执行效率慢等缺点;同时将数字孪生引入到碰撞检测过程中,建立起虚实系统之间的数据交互,实现对抓取系统的实时监测。最后,基于CoppeliaSim软件搭建了机械臂孪生仿真平台并开展了仿真实验。结果表明:提出的检测方法具有抓取精度高、稳定性强及可靠性高的优势。

    Abstract:

    During the operation of the manipulator,it is prone to collision among the manipulators and manipulator with people and objects in the external environment,resulting in property damage or injury or even death.In view of this,after comparing and analyzing the current mainstream path planning algorithms,the convolutional neural network and the RRT Connect algorithm were fused to make up for the shortcomings of the original algorithm,such as strong randomness and slow algorithm execution efficiency,and the digital twin was introduced into the collision detection process,the data interaction between the virtual and real systems was established to realize the real-time monitoring of the grasping system.Finally,based on CoppeliaSim software,a manipulator twin simulation platform was built and the simulation experiments were carried out.The results show that the proposed detection method has the advantages of high grasping accuracy,strong stability and high reliability.

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苏赫朋,苗鸿宾,纪慧君,申光鹏,余浪.面向数字孪生的机械臂抓取系统碰撞检测方法的研究[J].机床与液压,2024,52(8):159-166.
SU Hepeng, MIAO Hongbin, JI Huijun, SHEN Guangpeng, YU Lang. Research on Collision Detection Method of Grasping System for Digital Twin Manipulator[J]. Machine Tool & Hydraulics,2024,52(8):159-166

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  • 在线发布日期: 2024-04-29
  • 出版日期: 2024-04-28