论文标题
在密集混乱的环境中对新物体的实时抓取姿势估计
Real-time Grasp Pose Estimation for Novel Objects in Densely Cluttered Environment
论文作者
论文摘要
在Pick and Place应用中掌握新颖的物体是机器人技术中的一个基本且具有挑战性的问题，特别是用于复杂形的对象。据观察，诸如\ textIt {i}）从对象的质心和\ textit {ii}）抓住的众所周知的策略沿对象的主要轴抓住了复杂形状的对象通常会失败。在本文中，提出了在机器人拾取和位置应用中针对新物体的实时掌握姿势估计策略。提出的技术估计点云中的对象轮廓，并预测抓握姿势以及图像平面中的对象骨架。测试了该技术，例如球容器，手重，网球，甚至是Bloder（非凸形形状）等复杂形状对象。据观察，所提出的策略对于复杂形状的对象表现良好，并与上述策略相比预测了有效的掌握配置。当对象被明显地放置在物体中时，将所提出的抓地力技术的实验验证在两种情况下进行测试。掌握精度分别为88.16 \％和77.03 \％。所有实验均使用真实的UR10机器人操纵器以及WSG-50的两指抓手进行抓握。
Grasping of novel objects in pick and place applications is a fundamental and challenging problem in robotics, specifically for complex-shaped objects. It is observed that the well-known strategies like \textit{i}) grasping from the centroid of object and \textit{ii}) grasping along the major axis of the object often fails for complex-shaped objects. In this paper, a real-time grasp pose estimation strategy for novel objects in robotic pick and place applications is proposed. The proposed technique estimates the object contour in the point cloud and predicts the grasp pose along with the object skeleton in the image plane. The technique is tested for the objects like ball container, hand weight, tennis ball and even for complex shape objects like blower (non-convex shape). It is observed that the proposed strategy performs very well for complex shaped objects and predicts the valid grasp configurations in comparison with the above strategies. The experimental validation of the proposed grasping technique is tested in two scenarios, when the objects are placed distinctly and when the objects are placed in dense clutter. A grasp accuracy of 88.16\% and 77.03\% respectively are reported. All the experiments are performed with a real UR10 robot manipulator along with WSG-50 two-finger gripper for grasping of objects.
