Abstract—Grasping an unstructured object and setting the required air pressure is a significant problem for a soft robotic gripper. However, most extant Soft Robotic Grippers struggle to create this function automatically and efficiently. This article develops a new approach to an automated control method for a gripper using the NI Vision Builder Automated Inspection (VBAI) to create an intelligent robotic gripper based on the LabVIEW program. Machine vision and object classification methods were used in this experiment to get information about each object to be gripped. This system has collaborated between measurement and gripping tasks in real-time. Using the state diagram design, detecting and classifying objects at the point of placement found that the state diagram can detect and categorize all measured things precisely according to their actual size with an accuracy of ±0.5 millimeters. Furthermore, from the data obtained by utilizing the NI Distributed system manager feature to transmit data in realtime into the gripper control program, it was found that the gripper can grip perfectly with the automation system that has been built.
 
Index Terms—autonomous assembly, machine vision, soft robotic gripper, object detection, automated inspection

Cite: Chin-Yi Cheng, Jhy-Chyang Renn, Ilham Saputra, and Chen-En Shi, "Smart Grasping of a Soft Robotic Gripper Using NI Vision Builder Automated Inspection Based on LabVIEW Program," International Journal of Mechanical Engineering and Robotics Research, Vol. 11, No. 10, pp. 737-744, October 2022. DOI: 10.18178/ijmerr.11.10.737-744

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