Toward cloud-based grasping with uncertainty in shape: Estimating lower bounds on achieving force closure with zero-slip push grasps

This paper explores how Cloud Computing can facilitate grasping with shape uncertainty. We consider the most common robot gripper: a pair of thin parallel jaws, and a class of objects that can be modeled as extruded polygons. We model a conservative class of push-grasps that can enhance object alignment. The grasp planning algorithm takes as input an approximate object outline and Gaussian uncertainty around each vertex and center of mass. We define a grasp quality metric based on a lower bound on the probability of achieving force closure. We present a highly-parallelizable algorithm to compute this metric using Monte Carlo sampling. The algorithm uses Coulomb frictional grasp mechanics and a fast geometric test for conservative conditions for force closure. We run the algorithm on a set of sample shapes and compare the grasps with those from a planner that does not model shape uncertainty. We report computation times with single and multi-core computers and sensitivity analysis on algorithm parameters. We also describe physical grasp experiments using the Willow Garage PR2 robot.