  Robotic grasping of arbitrary objects even in completely known environments
still remains a challenging problem. Most previously developed algorithms had
focused on fingertip grasp, failing to solve the problem even for fully
actuated hands/grippers during adaptive/wrapping type of grasps, where each
finger makes contact with object at several points. Kinematic closed form
solutions are not possible for such an articulated finger which simultaneously
reaches several given goal points. This paper, presents a framework for
computing best grasp for an underactuated robotic gripper, based on a novel
object slicing method. The proposed method quickly find contacts using an
object slicing technique and use grasp quality measure to find the best grasp
from a pool of grasps. To validate the proposed method, implementation has been
done on twenty-four household objects and toys using a two finger underactuated
robot gripper. Unlike the many other existing approaches, the proposed approach
has several advantages: it can handle objects with complex shapes and sizes; it
does not require simplifying the objects into primitive geometric shape; Most
importantly, it can be applied on point clouds taken using depth sensor; it
takes into account gripper kinematic constraints and generates feasible grasps
for both adaptive/enveloping and fingertip types of grasps.
