Computation and Selection of Secure Gravity Based Caging Grasps of Planar Objects

Abstract

Gravity based caging grasps are robotic grasps where the robot hand passively supports an object against gravity. When a robot hand supports an object at a local minimum of the object gravitational energy, the robot hand forms a basket like grasp of the object. Any object movement in a basket grasp requires an increase of the object gravitational energy, thus allowing secure object pickup and transport with robot hands that use a small number fingers. The basket grasp depth measures the minimal additional energy the object must acquire to escape the basket grasp. This paper describes a computation scheme that determines the depth of entire sets of candidate basket grasps associated with alternative finger placements on the object boundary before pickup. The computation relies on categorization of escape stances that mark the basket grasp depth: double-support escapes are first analyzed and computed, then single-support escapes are analyzed and computed. The minimum energy combination of both types of escape stances defines the depth of entire sets of candidate basket grasps, which is then used to identify the deepest and hence most secure basket grasp. The computation scheme is fully implemented and demonstrated on several examples with reported run-times.