Suppr超能文献

基于导电热塑性弹性体的通用夹持器的触觉传感应用。

Tactile Sensing Applied to the Universal Gripper Using Conductive Thermoplastic Elastomer.

机构信息

Bio-Inspired Robotics Laboratory, Department of Engineering, University of Cambridge , Cambridge, United Kingdom .

出版信息

Soft Robot. 2018 Oct;5(5):512-526. doi: 10.1089/soro.2017.0089. Epub 2018 Sep 26.

Abstract

There is increasing interest in the use of soft materials in robotic applications ranging from wearable devices to soft grippers. While soft structures provide a number of favorable properties to robotic systems, sensing of large deformable soft structures is still a considerable challenge; sensors must not inhibit the mechanical properties of the soft body, and the potential infinite degree-of-freedom deformations mean that there is an intrinsically limited resolution of the sensing receptors. An approach to address these challenges using a conductive thermoplastic elastomer is proposed. This allows sensory strain information to be gained from deforming structures without disturbing the dynamics of the system enabling coverage of large soft surfaces. In this article, a theoretical framework is developed, which provides a set of design principles to optimize and characterize sensor implementation, allowing maximum information about location, posture, and shape of the object to be determined. The proposed approach has been tested experimentally for the case study of the universal gripper; investigating how a sensorized gripper can allow a robot to identify grasped objects to enable improved gripping and manipulation performance.

摘要

人们对软材料在机器人应用中的使用越来越感兴趣,从可穿戴设备到软夹爪。虽然软结构为机器人系统提供了许多优良的特性,但对大变形软结构的传感仍然是一个相当大的挑战;传感器不能抑制软物体的机械性能,潜在的无限自由度变形意味着传感受体的固有分辨率有限。提出了一种使用导电热塑性弹性体来解决这些挑战的方法。这使得可以从变形结构中获得传感应变信息,而不会干扰系统的动态,从而能够覆盖大的软表面。在本文中,提出了一种理论框架,该框架提供了一组设计原则来优化和表征传感器的实现,允许确定关于物体的位置、姿势和形状的最大信息。所提出的方法已经通过通用夹爪的案例研究进行了实验测试;研究了传感器化夹爪如何使机器人能够识别被抓取的物体,从而实现更好的抓取和操作性能。

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