Purpose

This paper aims to review the shape sensing techniques using large area flexible electronics (LAFE). Shape perception of humanoid robots using tactile data is mainly focused.

Design/methodology/approach

Research papers on different shape sensing methodologies of objects with large area, published in the past 15 years, are reviewed with emphasis on contact-based shape sensors. Fiber optics based shape sensing methodology is discussed for comparison purpose.

Findings

LAFE-based shape sensors of humanoid robots incorporating advanced computational data handling techniques such as neural networks and machine learning (ML) algorithms are observed to give results with best resolution in 3D shape reconstruction.

Research limitations/implications

The literature review is limited to shape sensing application either two- or three-dimensional (3D) LAFE. Optical shape sensing is briefly discussed which is widely used for small area. Optical scanners provide the best 3D shape reconstruction in the noncontact-based shape sensing; here this paper focuses only on contact-based shape sensing.

Practical implications

Contact-based shape sensing using polymer nanocomposites is a very economical solution as compared to optical 3D scanners. Although optical 3D scanners can provide a high resolution and fast scan of the 3D shape of the object, they require line of sight and complex image reconstruction algorithms. Using LAFE larger objects can be scanned with ML and basic electronic circuitory, which reduces the price hugely.

Social implications

LAFE can be used as a wearable sensor to monitor critical biological parameters. They can be used to detect shape of large body parts and aid in designing prosthetic devices. Tactile sensing in humanoid robots is accomplished by electronic skin of the robot which is a prime example of human–machine interface at workplace.

Originality/value

This paper reviews a unique feature of LAFE in shape sensing of large area objects. It provides insights from mechanical, electrical, hardware and software perspective in the sensor design. The most suitable approach for large object shape sensing using LAFE is also suggested.

中文翻译：

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大面积柔性电子器件在形状传感应用中的最新趋势和作用——综述

目的

本文旨在回顾使用大面积柔性电子设备 (LAFE) 的形状传感技术。主要关注使用触觉数据的仿人机器人的形状感知。

设计/方法/方法

回顾了过去 15 年发表的关于大面积物体不同形状传感方法的研究论文，重点是基于接触的形状传感器。为了比较目的，讨论了基于光纤的形状感测方法。

发现

观察到基于 LAFE 的类人机器人形状传感器结合了神经网络和机器学习 (ML) 算法等先进的计算数据处理技术，可在 3D 形状重建中提供最佳分辨率的结果。

研究限制/影响

文献综述仅限于二维或三维 (3D) LAFE 的形状传感应用。简要讨论了广泛用于小区域的光学形状传感。光学扫描仪在基于非接触的形状传感中提供最佳的 3D 形状重建；本文仅关注基于接触的形状传感。

实际影响

与光学 3D 扫描仪相比，使用聚合物纳米复合材料的基于接触的形状传感是一种非常经济的解决方案。虽然光学 3D 扫描仪可以提供物体 3D 形状的高分辨率和快速扫描，但它们需要视线和复杂的图像重建算法。使用 LAFE 可以使用 ML 和基本电子电路扫描较大的物体，这大大降低了价格。

社会影响

LAFE 可用作可穿戴传感器来监测关键生物参数。它们可用于检测大型身体部位的形状并帮助设计假肢装置。仿人机器人的触觉感知是通过机器人的电子皮肤完成的，这是工作场所人机界面的一个主要例子。

原创性/价值

本文回顾了 LAFE 在大面积物体形状传感方面的独特功能。它提供了传感器设计中机械、电气、硬件和软件方面的见解。还建议了使用 LAFE 进行大型物体形状感测的最合适方法。