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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 calculation. 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 on on human experience. Instead of focusing on individual objects, our method aware graspable components on the category level under the assumption that geometrically alike objects 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 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 d emonstrate the effectiveness of the proposed approach by realizing grasps on the graspable components of human choice for objects with complex geometry.