An overview of 3D object grasp synthesis algorithms

This overview presents computational algorithms for generating 3D object grasps with autonomous multi-fingered robotic hands. Robotic grasping has been an active research subject for decades, and a great deal of effort has been spent on grasp synthesis algorithms. Existing papers focus on reviewing the mechanics of grasping and the finger–object contact interactions Bicchi and Kumar (2000) [12] or robot hand design and their control Al-Gallaf et al. (1993) [70]. Robot grasp synthesis algorithms have been reviewed in Shimoga (1996) [71], but since then an important progress has been made toward applying learning techniques to the grasping problem. This overview focuses on analytical as well as empirical grasp synthesis approaches.

► We present a survey of computational algorithms for generating 3D object grasps.
► We classify works into two broad approaches: analytical and empirical methods.
► Analytical approaches use kinematic and dynamic formulations of the problem.
► Empirical grasping approaches are based on classification and learning methods.