基于双位置特征的无触觉传感器气动软机械手指的机械感知能力

Kai Shi, Jun Li, Gang Bao
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引用次数: 0

摘要

目的机械感知对于机器人规划和控制应用至关重要,而对于机器人手指来说,机械感知通常是通过触觉传感器获得的。作为一种新型机器人手指,软手指也需要机械感知,如接触力和物体硬度。与刚性手指不同,软手指具有弹性结构,这意味着力与软手指的变形之间存在联系。这使得软手指可以在不使用触觉传感器的情况下实现机械感知。本研究旨在提供一种不使用任何触觉传感器的软手指机械感知传感方案。本研究使用弯曲传感器测量受力时的实际弯曲状态,并利用压力传感器和静力学模型计算假定空载条件下的虚拟弯曲状态。手指虚拟状态和实际状态的区别在于手指在负载下的变形,其与手指刚度的乘积可用于计算接触力。在加压过程中,手指虚拟状态和实际状态的变化率是有区别的。在 0-6 N 的接触力范围内,接触力感知的检测精度可达 0.117 N 的均方根误差。接触物体刚度感知的检测平均偏差约为 15%,低刚度物体的检测标准偏差为 10%,高刚度物体的检测标准偏差为 20%。本文提出了一种通用的软手指机械感知方法,该方法只使用不可或缺的弯曲和压力传感器,而不使用触觉传感器。它有助于降低软体机器人的硬件复杂性。同时,软手指不再需要在指尖部署触觉传感器,这有利于指尖结构的优化设计,而无需考虑复杂的传感器安装。另一方面,这种方法不再局限于增加所需的组件。它可以充分利用机器人软体的物理弹性来转换传感器信号。从本质上讲,它将软执行器视为软传感器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mechanoreception of pneumatic soft robotic finger without tactile sensor based on dual-position feature

Purpose

Mechanoreception is crucial for robotic planning and control applications, and for robotic fingers, mechanoreception is generally obtained through tactile sensors. As a new type of robotic finger, the soft finger also requires mechanoreception, like contact force and object stiffness. Unlike rigid fingers, soft fingers have elastic structures, meaning there is a connection between force and deformation of the soft fingers. It allows soft fingers to achieve mechanoreception without using tactile sensors. This study aims to provide a mechanoreception sensing scheme of the soft finger without any tactile sensors.

Design/methodology/approach

This research uses bending sensors to measure the actual bending state under force and calculates the virtual bending state under assumed no-load conditions using pressure sensors and statics model. The difference between the virtual and actual finger states is the finger deformation under load, and its product with the finger stiffness can be used to calculate the contact force. There are distinctions between the virtual and actual finger state change rates in the pressing process. The difference caused by the stiffness of different objects is different, which can be used to identify the object stiffness.

Findings

Contact force perception can achieve a detection accuracy of 0.117 N root mean square error within the range of 0–6 N contact force. The contact object stiffness perception has a detection average deviation of about 15%, and the detection standard deviation is 10% for low-stiffness objects and 20% for high-stiffness objects. It performs better at detecting the stiffness of low-stiffness objects, which is consistent with the sensory ability of human fingers.

Originality/value

This paper proposes a universal mechanoreception method for soft fingers that only uses indispensable bending and pressure sensors without tactile sensors. It helps to reduce the hardware complexity of soft robots. Meanwhile, the soft finger no longer needs to deploy the tactile sensor at the fingertip, which can benefit the optimization design of the fingertip structure without considering the complex sensor installation. On the other hand, this approach is no longer confined to adding components needed. It can fully use the soft robot body’s physical elasticity to convert sensor signals. Essentially, It treats the soft actuators as soft sensors.

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