  The rise in additive manufacturing comes with unique opportunities and
challenges. Massive part customization and rapid design changes are made
possible with additive manufacturing, however, manufacturing industries that
desire the implementation of robotics automation to improve production
efficiency could face challenges in the gripper design and grasp planning due
to highly complex geometrical shapes resulting from massive part customization.
Yet, current gripper design for such objects are often manual and rely on
ad-hoc design intuition. This would be limiting as such grippers would lack the
ability to grasp different objects or grasp points, which is important for
practical implementations. Hence, we introduce a fast, end-to-end approach to
customize rigid gripper fingerpads that could achieve precise and stable
grasping for different objects at multiple grasp points. Our approach relies on
two key components: (i) a method based on set Boolean operations, e.g.
intersections, subtractions, and unions to extract object features and
synthesize gripper surfaces that conform to different local shapes to form
caging grasps; (ii) a method to evaluate the grasp quality of synthesized
grippers. We experimentally demonstrate the validity of our approach by
synthesizing fingerpads that, once mounted on a physical robot gripper, are
able to grasp different objects at multiple grasp points, all with tightly
constrained grasps.
