6D pose estimation and robotic arm grasping method for weakly rextured workpiece
WAN Qin
NING Shun-xing
ZHONG Hang
HE Yong
DUAN Xiao-gang
WANG Yao-nan
WU Di
SHEN Xue-jun
Abstract：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 a fitness 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.
Keywords：deep learning6D pose estimationtarget detectiontrajectory planningrobotic arm grasping
Publication Date：2025-07-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 1443-1452 )