Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei 10608, Taiwan.
Sensors (Basel). 2023 Jan 17;23(3):1076. doi: 10.3390/s23031076.
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.
本研究专注于使用 RGB-D 图像和修改现有的机器学习网络架构来生成成功抓取物体位置的预测，并优化用于状态延迟的控制系统。设计了一个五指夹持器来模拟人类手掌，以证明它可以执行比许多两指或三指夹持器更精细的任务。使用具有五指和两指夹持器的 6-DOF 机器人臂进行了实验，以进行至少 100 次实际机器抓取，并与其他研究的结果进行了比较。此外，我们研究了状态时滞并提出了一种机器人操纵器的控制方法。已经进行了许多关于时滞系统的研究，但大多数研究都集中在输入和输出延迟上。强调这一点的一个原因是输入和输出延迟是物理或电子系统中最常见的延迟。另一个原因是状态延迟增加了整个控制系统的复杂性。最后，结果表明，我们的网络可以在很少的训练数据和省略步骤（如姿势评估）的情况下与深度网络架构一样表现良好，并且当与五指夹持器的硬件优势结合使用时，它可以产生一个自动化系统，其抓取成功率超过 90%。本文是会议论文的扩展研究。
