面向弱纹理工件的6D位姿估计与机械臂抓取方法

6D pose estimation and robotic arm grasping method for weakly rextured workpiece

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摘要针对复杂工业场景中机械臂难以对弱纹理工件进行有效抓取的问题,本文提出了一种面向弱纹理工件的6D位姿估计与机械臂抓取方法.首先,为提高弱纹理工件6D位姿估计的准确性,结合YOLOV5和PVN3D-Tiny提出了一种新的两阶段位姿估计算法(YOLO-PVN3D);其次,采用七次多项式插值法规划机械臂运动轨迹,根据碰撞检测参数和运动学指标建立适应度函数,并通过遗传算法进行优化,以解决抓取过程中机械臂与障碍物产生碰撞的问题;然后,针对真实数据匮乏且容易造成模型过拟合的问题,采用了真实数据和合成数据相结合的方式制作了工业零件数据集POSE8K;最后,在公共数据集和自制数据集进行了对比实验,并在障碍物遮挡和光照变化场景下完成了真实机械臂抓取实验.经实验验证了所提方法具有较好的性能. In order to solve the problem that it is difﬁcult for robotic arm to effectively grasp weakly textured workpieces in complex industrial scenarios.This paper proposes a 6D pose estimation and robotic arm grasping method for weakly tex-tured workpieces.Firstly,a new two-stage pose estimation algorithm(YOLO-PVN3D)is proposed by combining YOLOV5 and PVN3D-Tiny to improve the accuracy of 6D pose estimation for weakly textured workpieces.Then,the seventh-order polynomial interpolation method is adopted to plan the movement trajectory of the manipulator,integrate the collision detection results and kinematic indicators to establish aﬁtness function,and optimize it through a genetic algorithm to solve the problem of collision between robotic arm and obstacles during gripping process.Moreover,a datasets POSE8K is created by combining real data and synthetic data to tackle the problem of insufﬁcient real-world data and the risk of model overﬁtting.Finally,comparative experiments were conducted on public datasets and the custom datasets.In addition real-world robot grasping experiments were performed in scenarios with occlusions and varying lighting conditions.The experimental results demonstrate that the proposed method achieves superior performance.

作者万琴宁顺兴钟杭何勇段小刚王耀南吴迪沈学军 WAN Qin;NING Shun-xing;ZHONG Hang;HE Yong;DUAN Xiao-gang;WANG Yao-nan;WU Di;SHEN Xue-jun(College of Electrical&Information Engineering,Hunan Institute of Engineering,Xiangtan Hunan 411104,China;National Engineering Research Laboratory for Robot Vision Perception and Control,Hunan University,Changsha Hunan 410082,China;Hunan Zhongnan Intelligent Equipment Co.,Ltd,Changsha Hunan 410117,China)

机构地区湖南工程学院电气与信息工程学院湖南大学机器人视觉感知与控制技术国家工程实验室湖南中南智能装备有限公司

出处《控制理论与应用》北大核心 2025年第7期1443-1452,共10页 Control Theory & Applications

基金国家自然科学基金青年项目(62006075) 湖南省重点研发计划项目(2021GK2024) 湖南省杰出青年科学基金项目(2021JJ10002) 湖南省自然科学基金项目(2022JJ30198) 湖南省教育厅项目(21A0460) 湖南省研究生科技创新一般项目(CX20231287)资助。

关键词深度学习 6D位姿估计目标检测轨迹规划机械臂抓取 deep learning 6D pose estimation target detection trajectory planning robotic arm grasping

分类号 TP391.41 [自动化与计算机技术—计算机应用技术]