Department of Biomedical Engineering, School of Engineering and Applied Science, George Washington University, Washington, DC 20052, USA.
School of Computer Science and Electrical Engineering, Handong Global University, Pohang 37554, Republic of Korea.
Sensors (Basel). 2024 Mar 8;24(6):1751. doi: 10.3390/s24061751.
This study investigates the characteristics of a novel origami-based, elastomeric actuator and a soft gripper, which are controlled by hand gestures that are recognized through machine learning algorithms. The lightweight paper-elastomer structure employed in this research exhibits distinct actuation features in four key areas: (1) It requires approximately 20% less pressure for the same bending amplitude compared to pneumatic network actuators (Pneu-Net) of equivalent weight, and even less pressure compared to other actuators with non-linear bending behavior; (2) The control of the device is examined by validating the relationship between pressure and the bending angle, as well as the interaction force and pressure at a fixed bending angle; (3) A soft robotic gripper comprising three actuators is designed. Enveloping and pinch grasping experiments are conducted on various shapes, which demonstrate the gripper's potential in handling a wide range of objects for numerous applications; and (4) A gesture recognition algorithm is developed to control the gripper using electromyogram (EMG) signals from the user's muscles.
这项研究调查了一种基于折纸的新型弹性体致动器和软夹爪的特性，它们通过机器学习算法识别的手势进行控制。本研究中使用的轻质纸弹性体结构在四个关键领域具有明显的致动特性：（1）与等效重量的气动网络致动器（Pneu-Net）相比，它需要的压力大约少 20%，与具有非线性弯曲行为的其他致动器相比，压力甚至更小；（2）通过验证压力与弯曲角度之间的关系以及在固定弯曲角度下的相互作用力与压力之间的关系，来检验设备的控制；（3）设计了一个由三个致动器组成的软机器人夹爪。对各种形状进行了包裹和捏合抓取实验，展示了该夹爪在处理各种形状的物体方面的潜力，可用于许多应用；（4）开发了一种手势识别算法，通过用户肌肉的肌电图（EMG）信号来控制夹爪。
