Fast grasping of unknown objects using force balance optimization

Abstract

Grasping of unknown objects (neither appearance data nor object models are given in advance) is very important for robots that work in an unfamiliar environment. In this paper, in order to make grasping of unknown objects more reliable and faster, we propose a novel grasping algorithm which does not require to build a 3D model of the object. For most objects, one point cloud is enough. For other objects, at most two point clouds are enough to synthesize reliable grasp. Taking grasping range and width of robot hand into consideration, the most suitable grasping region can be calculated on the contour of the point cloud of unknown object by maximizing the coefficient of force balance. Further analysis of the point cloud in the best grasping region can obtain the grasping position and orientation of robot hand. The point cloud information is processed on line, the grasping algorithm can quickly get the grasping position and orientation and then drive robot to the grasping point to execute grasping action. Simulations and experiments on an Universal arm UR5 and an underactuated Lacquey Fetch gripper are used to examine the performance of the algorithm, and successful results are obtained.