Han Jinghai, Liu Bo, Jia Yongle, Jin Shoufeng, Sulowicz Maciej, Glowacz Adam, Królczyk Grzegorz, Li Zhixiong
Institute of Rail Transport, Nanjing Vocational Institute of Transport Technology, Nanjing 211188, China.
College of Mechanical and Electrical Engineering, Xi'an Polytechnic University, Xi'an 710600, China.
Micromachines (Basel). 2022 May 31;13(6):886. doi: 10.3390/mi13060886.
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 10 s, and the grasping success rate is above 80%, which meets the industrial production requirements.
这项工作提出了一种基于Kinect V2的视觉方法，以解决抓取操作中人类对纱筒机器人的依赖问题。在这种新方法中，使用Kinect V2相机在工作场景中为机器人生成三维（3D）纱筒点云数据。通过适当的滤波过程去除噪声点云后，采用M估计样本一致性（MSAC）算法来找到3D云数据的拟合平面；然后，采用主成分分析（PCA）对模板点云和纱筒点云进行粗略配准，以定义纱筒的初始位置。最后，使用迭代最近点（ICP）算法实现3D云数据的精确配准，以确定纱筒的精确姿态。为了评估所提方法的性能，开发了一个实验平台来验证纱筒机器人在不同场景下的抓取操作。分析结果表明，机器人系统的平均工作时间在10秒以内，抓取成功率高于80%，满足工业生产要求。
