An effective method for grasp planning on objects with complex geometry combining human experience and analytical approach  

作　　者：Chunfang LIU Fuchun SUN Xiaojuan BAN 

机构地区：[1]Department of Computer Science and Technology, Tsinghua University [2]Department of Computer Science and Technology, University of Science and Technology Beijing

出　　处：《Science China(Information Sciences)》2016年第11期166-177,共12页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant Nos. 61210013, 61327809, 91420302, 91520201)

摘　　要：In this paper, an effective method for identifying the graspable components of objects with complex geometry is proposed for grasp planning based on human experience. Instead of focusing on individual objects, our method identifies graspable components on the category level under the assumption that geometrically alike objects share similar graspable components. Firstly, employing a modified SHOT descriptor, a fast KNN-based method is developed for object categorization. Then, the graspable components are identified by adopting a learning framework based on human experience. Afterwards, a fast analytical grasp planning method is proposed which comprises of contact points exaction and hand kinematics cMculation. Finally, a regression model based on the extreme learning method （ELM） is built which inputs the desired contact points and the wrist orientation and outputs the wrist position. This approach is time-saving comparing with the optimization method. The simulations and experiments demonstrate the effectiveness of the proposed approach by realizing grasps on the graspable components of human choice for objects with complex geometry.In this paper, an effective method for identifying the graspable components of objects with complex geometry is proposed for grasp planning based on human experience. Instead of focusing on individual objects, our method identifies graspable components on the category level under the assumption that geometrically alike objects share similar graspable components. Firstly, employing a modified SHOT descriptor, a fast KNN-based method is developed for object categorization. Then, the graspable components are identified by adopting a learning framework based on human experience. Afterwards, a fast analytical grasp planning method is proposed which comprises of contact points exaction and hand kinematics cMculation. Finally, a regression model based on the extreme learning method （ELM） is built which inputs the desired contact points and the wrist orientation and outputs the wrist position. This approach is time-saving comparing with the optimization method. The simulations and experiments demonstrate the effectiveness of the proposed approach by realizing grasps on the graspable components of human choice for objects with complex geometry.

关 键 词：grasp planning human experience analytical method kinematics learning ELM 

分 类 号：TP241[自动化与计算机技术—检测技术与自动化装置]