A system of robotic grasping with experience acquisition  

作　　者：GUO Di SUN FuChun LIU ChunFang 

机构地区：[1]State Key Laboratory of Intelligence Technology and Systems,Department of Computer Science and Technology,Tsinghua Univerisity

出　　处：《Science China(Information Sciences)》2014年第12期14-24,共11页中国科学（信息科学）（英文版）

基　　金：supported by Major International(Regional)Joint Research Project,National Natural Science Foundation of China(Grant No.61210013);NSF of China Project Major Instrument,Principal Investigator,National Natural Science Foundation of China(Grant No.61327809)

摘　　要：Robotic grasping has played a fundamental role in the robotic manipulation, while grasping an unknown object is still a challenge. A successful grasp is largely determined by the object representation and the corresponding grasp planning strategy. With the help of RGBD camera, the point cloud of the object can be obtained conveniently. However, the large amount of point cloud is often unorganized with some inevitable noise. It may result in the geometry of the object imprecise and lead to some poor grasp planning. In this paper, a parametric model--superquadric is chosen to represent the shape of an object. We firstly recover the superquadric of an object from the raw point cloud in a single view with conjugate gradient method. Then a force-closure grasp planning strategy is applied to this object to obtain stable grasp configurations. Finally we store the grasp parameters as grasp experience in a grasp dataset which can be used for future grasping tasks. The performance of the proposed grasping system is represented both in simulation and actual experiment scenario successfully.Robotic grasping has played a fundamental role in the robotic manipulation, while grasping an unknown object is still a challenge. A successful grasp is largely determined by the object representation and the corresponding grasp planning strategy. With the help of RGBD camera, the point cloud of the object can be obtained conveniently. However, the large amount of point cloud is often unorganized with some inevitable noise. It may result in the geometry of the object imprecise and lead to some poor grasp planning. In this paper, a parametric model--superquadric is chosen to represent the shape of an object. We firstly recover the superquadric of an object from the raw point cloud in a single view with conjugate gradient method. Then a force-closure grasp planning strategy is applied to this object to obtain stable grasp configurations. Finally we store the grasp parameters as grasp experience in a grasp dataset which can be used for future grasping tasks. The performance of the proposed grasping system is represented both in simulation and actual experiment scenario successfully.

关 键 词：object representation grasp planning experience acquisition point cloud superquadric recovery force closure grasp 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] P631.43[自动化与计算机技术—控制科学与工程]