A New Kinect V2-Based Method for Visual Recognition and Grasping of a Yarn-Bobbin-Handling Robot

Abstract

This work proposes a Kinect V2-based visual method to solve the human dependence on the yarn bobbin robot in the grabbing operation. In this new method, a Kinect V2 camera is used to produce three-dimensional (3D) yarn-bobbin point cloud data for the robot in a work scenario. After removing the noise point cloud through a proper filtering process, the M-estimator sample consensus (MSAC) algorithm is employed to find the fitting plane of the 3D cloud data; then, the principal component analysis (PCA) is adopted to roughly register the template point cloud and the yarn-bobbin point cloud to define the initial position of the yarn bobbin. Lastly, the iterative closest point (ICP) algorithm is used to achieve precise registration of the 3D cloud data to determine the precise pose of the yarn bobbin. To evaluate the performance of the proposed method, an experimental platform is developed to validate the grabbing operation of the yarn bobbin robot in different scenarios. The analysis results show that the average working time of the robot system is within 10s, and the grasping success rate is above 80%, which meets the industrial production requirements.

Automatic loading method for robot guided by 3D vision for scattered and stacked shoe soles

“…Some experts and scholars have conducted research on posture estimation methods for robot grasping using 3D vision technology. Han et al [7] obtained yarn point cloud data with a Kinect V2 camera, used point cloud filtering and the MSAC algorithm to remove noise, and found the fitting plane, followed by registering the target yarn point cloud using the PCA-ICP algorithm to obtain the precise pose of the yarn cylinder. Hinterstoisser et al [8] proposed a template matching method based on image gradient information and point cloud normal features to obtain the pose of the grasping target in real time.…”

Kernel extreme learning machine‐based general solution to forward kinematics of parallel robots

“…Parallel robots, in comparison with their serial counterpart, offer intrinsic advantages of high precision, high stiffness and high loading capability [1], therefore, they have been extensively applied and brought breakthroughs in numerous fields such as the motion generator [2], parallel machine tools [3–5], antenna mount [6], sensors [7], manufacturing [8, 9] and rehabilitation exercises [10–12]. The motion‐planning and control problem play crucial roles in the field of robotics regardless of serial robots or parallel robots [13–15]. With the further research of parallel robots, it is necessary to cope with the forward kinematics of parallel robots for their feedback control (as shown in Figure 1a) [16], workspace, calibration (Figure 1b) [17, 18] and many other potential applications.…”