A study on data-driven in-hand twisting process using a novel dexterous robotic gripper for assembly automation

Abstract

In electronic manufacturing system, the design of the robotic hand with sufficient dexterity and configuration is important for the successful accomplishment of the assembly task. It is significant that the robot can grasp assembly parts and do some simple in-hand manipulation so as to fit them with the package slots. In this research, we study the process of precise in-hand posture transition problem using a novel jaw like gripper with human-sized anthropomorphic features. We transform the in-hand manipulation problem into a series of static grasping problems. Then we study the successful twisting condition on each grasp frame by analyzing its dynamic performance and requirements. Based on this data-driven idea, simulation and experimental data is obtained from both successful and failed trials. Finally, we create the distribution of parameters grasp map for successful twisting.