论文标题
快速且坚固的bin选择系统，用于密集的堆积工业物体
Fast and Robust Bin-picking System for Densely Piled Industrial Objects
论文作者
论文摘要
物体抓紧，也称为bin挑选，是工业机器人面临的最常见任务之一。尽管相关主题已经完成了许多工作，但随机堆积的对象仍然是一个挑战，因为许多现有工作要么缺乏健壮性或成本过多。在本文中，我们开发了一个快速，坚固的bin选择系统，用于自适应地抓住密集的堆积物体。提出的系统从点云进行开始，使用基于噪声（DBSCAN）算法的基于密度的空间聚类（DBSCAN）算法进行了改进，该算法通过组合生长算法并使用OCTREE来加快计算加快计算来改进。然后，该系统使用原理分析（PCA）进行粗登记和迭代最接近点（ICP）进行精细注册。我们提出了一个掌握风险评分（GRS），以通过碰撞概率，对象的稳定性以及整个堆的稳定性来评估每个对象。通过使用Anno机器人进行的真实测试，我们的方法已被验证为速度和稳健性。
Objects grasping, also known as the bin-picking, is one of the most common tasks faced by industrial robots. While much work has been done in related topics, grasping randomly piled objects still remains a challenge because much of the existing work either lack robustness or costs too much resource. In this paper, we develop a fast and robust bin-picking system for grasping densely piled objects adaptively and safely. The proposed system starts with point cloud segmentation using improved density-based spatial clustering of application with noise (DBSCAN) algorithm, which is improved by combining the region growing algorithm and using Octree to speed up the calculation. The system then uses principle component analysis (PCA) for coarse registration and iterative closest point (ICP) for fine registration. We propose a grasp risk score (GRS) to evaluate each object by the collision probability, the stability of the object, and the whole pile's stability. Through real tests with the Anno robot, our method is verified to be advanced in speed and robustness.
