Grasping Unknown Objects Based on Gripper Workspace Spheres
Grasping Unknown Objects Based on Gripper Workspace Spheres
复制标题
基于夹具工作空间球体抓取未知物体
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
G. Neumann
中科院分区：
文献类型：
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作者：
M. Sorour;K. Elgeneidy;A. Srinivasan;Marc Hanheide;G. Neumann
本文提出了一种新的未知目标抓取规划算法，该算法从不同的角度给出了目标的注册点云。建议的方法不需要事先了解对象，也不需要离线学习。在我们的方法中，手爪运动学模型被用来生成每个手指工作空间的点云，然后用球体填充。在运行时，首先在垂直于其约束主要抓取动作的平面中分割对象，计算其长轴。然后对物体进行均匀采样和扫描，以寻找确保每个手指的工作空间中至少有一个物体点的各种抓手姿势。此外，与对象或工作台的碰撞检查使用计算上不昂贵的夹爪形状近似来执行。我们的方法既节省时间(平均消耗不到1.5秒)，又具有通用性。已经成功地在一个简单的下巴抓取器(Franka Panda抓取器)以及一个复杂的高自由度(DoF)手(快板手)上进行了实验。
In this paper, we present a novel grasp planning algorithm for unknown objects given a registered point cloud of the target from different views. The proposed methodology requires no prior knowledge of the object, nor offline learning. In our approach, the gripper kinematic model is used to generate a point cloud of each finger workspace, which is then filled with spheres. At run-time, first the object is segmented, its major axis is computed, in a plane perpendicular to which, the main grasping action is constrained. The object is then uniformly sampled and scanned for various gripper poses that assure at least one object point is located in the workspace of each finger. In addition, collision checks with the object or the table are performed using computationally inexpensive gripper shape approximation. Our methodology is both time efficient (consumes less than 1.5 seconds in average) and versatile. Successful experiments have been conducted on a simple jaw gripper (Franka Panda gripper) as well as a complex, high Degree of Freedom (DoF) hand (Allegro hand).