Eye-in-Hand Robotic Arm Gripping System Based on Machine Learning and State Delay Optimization

Abstract

This research focused on using RGB-D images and modifying an existing machine learning network architecture to generate predictions of the location of successfully grasped objects and to optimize the control system for state delays. A five-finger gripper designed to mimic the human palm was tested to demonstrate that it can perform more delicate missions than many two- or three-finger grippers. Experiments were conducted using the 6-DOF robot arm with the five-finger and two-finger grippers to perform at least 100 actual machine grasps, and compared to the results of other studies. Additionally, we investigated state time delays and proposed a control method for a robot manipulator. Many studies on time-delay systems have been conducted, but most focus on input and output delays. One reason for this emphasis is that input and output delays are the most commonly occurring delays in physical or electronic systems. An additional reason is that state delays increase the complexity of the overall control system. Finally, it was demonstrated that our network can perform as well as a deep network architecture with little training data and omitting steps, such as posture evaluation, and when combined with the hardware advantages of the five-finger gripper, it can produce an automated system with a gripping success rate of over 90%. This paper is an extended study of the conference paper.

Keywords

• point cloud
• neural network
• arm control
• object grabbing
• state delays