Abstract
The size of a device and its adaptability to human properties are important factors in developing a wearable device. In wearable robot research, therefore, soft materials and tendon transmissions have been utilized to make robots compact and adaptable to the human body. However, when used for wearable robots, these methods sometimes cause uncertainties that originate from elongation of the soft material or from undefined human properties. In this research, to consider these uncertainties, we propose a data-driven method that identifies both kinematic and stiffness parameters using tension and wire stroke of the actuators. Through kinematic identification, a method is proposed to find the exact joint position as a function of the joint angle. Through stiffness identification, the relationship between the actuation force and the joint angle is obtained using Gaussian Process Regression (GPR). As a result, by applying the proposed method to a specific robot, the research outlined in this paper verifies how the proposed method can be used in wearable robot applications. This work examines a novel wearable robot named Exo-Index, which assists a human’s index finger through the use of three actuators. The proposed identification methods enable control of the wearable robot to result in appropriate postures for grasping objects of different shapes and sizes.
Highlights
Due to significant improvements in actuation and sensing components in terms of size and performance, technologies for wearable devices have received great attention and have been developed for various purposes.Development of these devices requires an in-depth understanding of human properties, because these devices are intended to be worn on the human body
We propose a soft robotic glove named Exo-Index, which is controlled with consideration of the uncertainties that arise from human factors and the robot’s soft components without any wearable sensors
The proposed method is derived using data-driven method named Gaussian Process Regression (GPR) and the result of the method shows that it is sufficient to estimate the posture without additional sensors at the wearing part
Summary
Due to significant improvements in actuation and sensing components in terms of size and performance, technologies for wearable devices (e.g., haptic devices [1], wearable sensors [2,3], wearable robots [4,5,6,7]) have received great attention and have been developed for various purposes.Development of these devices requires an in-depth understanding of human properties, because these devices are intended to be worn on the human body. Due to significant improvements in actuation and sensing components in terms of size and performance, technologies for wearable devices (e.g., haptic devices [1], wearable sensors [2,3], wearable robots [4,5,6,7]) have received great attention and have been developed for various purposes. One way to address these issues is to use soft material; softness provides adaptability and is more comfortable to wear [8,9,10] In this approach, the size effect can be handled because a soft structure can fit well against the human body, even if there is a slight difference in size.
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