  Customized grippers have broad applications in industrial assembly lines.
Compared with general parallel grippers, the customized grippers have
specifically designed fingers to increase the contact area with the workpieces
and improve the grasp robustness. However, grasp planning for customized
grippers is challenging due to the object variations, surface contacts and
structural constraints of the grippers. In this paper, an iterative surface
fitting (ISF) algorithm is proposed to plan grasps for customized grippers. ISF
simultaneously searches for optimal gripper transformation and finger
displacement by minimizing the surface fitting error. A guided sampling is
introduced to avoid ISF getting stuck in local optima and improve the collision
avoidance performance. The proposed algorithm is able to consider the
structural constraints of the gripper and plan optimal grasps in real-time. The
effectiveness of the algorithm is verified by both simulations and experiments.
The experimental videos are available at:
http://me.berkeley.edu/\%7Eyongxiangfan/CASE2018/caseisf.html
