Citation: Yan, Y.; Cheng, C.; Guan,
M.; Zhang, J.; Wang, Y. Texture
Identification and Object Recognition
Using a Soft Robotic Hand
Innervated Bio-Inspired
Proprioception. Machines 2022, 10,
173. https://doi.org/10.3390/
machines10030173
Academic Editors: Yuansong Qiao
and Seamus Gordon
Received: 27 January 2022
Accepted: 24 February 2022
Published: 25 February 2022
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Article
Texture Identification and Object Recognition Using a Soft
Robotic Hand Innervated Bio-Inspired Proprioception
Yadong Yan
1
, Chang Cheng
2
, Mingjun Guan
1
, Jianan Zhang
1
and Yu Wang
1,
*
1
School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China;
adam7217@buaa.edu.cn (Y.Y.); sy1910110@buaa.edu.cn (M.G.); baby0303zjn@buaa.edu.cn (J.Z.)
2
Department of Mathematics and Computer Science, Colorado College, Colorado Springs, CO 80903, USA;
d_cheng@coloradocollege.edu
* Correspondence: wangyu@buaa.edu.cn
Abstract:
In this study, we innervated bio-inspired proprioception into a soft hand, facilitating a
robust perception of textures and object shapes. The tendon-driven soft finger with three joints, in-
spired by the human finger, was detailed. With tension sensors embedded in the tendon that simulate
the Golgi tendon organ of the human body, 17 types of textures can be identified under uncertain
rotation angles and actuator displacements. Four classifiers were used and the highest identification
accuracy was 98.3%. A three-fingered soft hand based on the bionic finger was developed. Its basic
grasp capability was tested experimentally. The soft hand can distinguish 10 types of objects that
vary in shape with top grasp and side grasp, with the highest accuracies of 96.33% and 96.00%,
respectively. Additionally, for six objects with close shapes, the soft hand obtained an identification
accuracy of 97.69% with a scan-grasp method. This study offers a novel bionic solution for the texture
identification and object recognition of soft manipulators.
Keywords: bionic proprioception; soft manipulator; texture identification; object recognition
1. Introduction
Soft manipulators have been widely studied due to their inherent compliance during
interactions with objects and the environment [
1
–
5
]. Most of them are generally driven by
pneumatic actuators [
1
,
2
], tendons [
3
,
4
] and Magneto-/electro-responsive polymers [
5
],
etc. Some studies have also realized delicate in-hand manipulations [
6
–
9
]. However,
adequately endowing robots with a “sense of touch” remains an unsolved challenge [
10
,
11
].
Considerable work has focused on flexible, surface-mountable tactile sensors to realize
texture recognition [
12
,
13
] and mimic the human cutaneous mechanoreceptive system to
achieve tactile sensation in robotic hands [
14
–
16
]. However, it is difficult to densely cover
the entire manipulator using these types of sensors. Even if array sensors are used in some
adaptive grippers to realize the perception of large areas [
17
,
18
], it leads to high cost, and
the sensitive area is still confined. Moreover, recalibration is generally required for changes
in contact conditions, such as the contact angle and actuate state [
19
]. All these drawbacks
limit the application of these sensors.
Recent work has shown that proprioception is an effective method by which to achieve
robotic hand sensory ability. In Zhao’s work, they innervated a soft robotic hand via optical
waveguides to detect the shapes and textures of objects [
20
]. In addition, an analogous
sensor was used to measure the curvature of the soft structure in [
21
]. By embedding bend
sensors in soft fingers, the soft hand in [
22
] was able to identify different objects that vary in
shape. Luca’s work reconstructed the shape of a soft finger using a hexagonal tactile array
placed at the base of the cylinder finger [
23
]. Additionally, the combination of machine
learning and the distributed proprioceptive sensors were used for the perceived shape of a
soft arm [24]. All these works demonstrate the potential of proprioception in soft robot.
Machines 2022, 10, 173. https://doi.org/10.3390/machines10030173 https://www.mdpi.com/journal/machines
