Integration of recognition and planning for robot hand grasping

Abstract

A robot should be able to recognize and estimate the pose of an object in order to grasp it. In addition, the robot should be able to infer the most reasonable strategy for grasping the object, which varies according to the type and pose of the object. In this paper, we design a grasping strategy engine for this purpose and suggest a method for recognizing and estimating the pose of an object with no two-dimensional intensity image. We also introduce our grasping data acquisition system (GDAS) for learning the best grasping strategy. The grasping strategy is composed of the approaching vector, opposition vector, and grasping type. In this paper, we use the iterative closest point (ICP) [1] algorithm for recognizing and estimating the pose of an object, along with an artificial neural network for selecting the best grasping strategy.