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, inspired 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.
Highlights
Soft manipulators have been widely studied due to their inherent compliance during interactions with objects and the environment [1–5]
For SVM-rbf, K nearest neighbor (KNN) and decision trees (DTs), the accuracies of DS3 decreased by 7.59%, 1.96% and 3.60%; the accuracies of DS4 decreased by 8.34%, 1.88% and 3.28%; and the accuracies of tDhSa5t tdheecrreaansdeodmbnye1s4s.o69f %th,e3f.8in3g%eranrodta6t.i2o9n%a,ncgolmespaafrfeedctweditthhDe rSe1s,urletssp. eTchteivceolyn,fiunsdioicnatminagttrhiaxt, tuhseinrganKdNomNnteosisdoefntthifeyfiDnSg5e,rirsosthatoiwonnainngFleigsuarffee1c1te,dwthhiechreascuhltise.vTehdethcoenhfiugshioenstmclaatsrsiix, fuisciantgioKnNacNcutoraicdyenintiftyhiDs St5a,skis. sFhoorwthneinSVFiMgu-lrien1e1a,rwclhaiscshifaiecrh,itehveedirtrheeguhliagrhechstacnlgasesiinficaactciounraacccyuriamcypliinedthtihs atatstkh.eFtoerxtthuereSsVaMre-lhinaeradr tcolalsisniefiaerrl,ythiedeirnretigfyulianr cahlaonwg-ediinmaecncsuiroancaylifmeaptluierde sthpaatceth. e textures are hard to linearly identify in a low-dimensional feature space
We can see that when the cylinders were labeled as soft and hard, the classifiers had the best performance, and the accuracies of SVM-rbf, KNN and DTs were all greater than 98%
Summary
Soft manipulators have been widely studied due to their inherent compliance during interactions with objects and the environment [1–5]. By embedding bend sensors in soft fingers, the soft hand in [22] was able to identify different objects that vary in shape. 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. Using a linear encoder to mimic the muscle spindle and tension sensor to mimic GTO, our system was able to achieve perception. We integrated it into a bionic finger and into a soft robotic hand.
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