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Abstract

People can rely on complex tactile systems to perceive the basic physical properties of an object, and take the corresponding action and pressure to grab the object. However, this task is not easy for mechanical manipulator. The feedback of most manipulators is limited to pressure, it is difficult for manipulators to identify the other properties of the object by touch. A new manipulator method is designed in this paper. The tactile images are reflected by flexible tactile sensor in this manipulator system. A large number of tactile images are used to build training set by which the capsule network is trained to evaluate images. When an object is captured by a manipulator, and parameters such as shape, rigidity, weight and density of target objects are analyzed. Pseudo color transformation of tactile images is performed, in this way, the changes of each parameter of tactile signals can be observed visibly. It is innovative to process the tactile image by using capsule network and realize the evolutionary bionic tactile function.

Keywords

Capsule network image processing bionic manipulator pseudo color image

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References

Motoi

, Shimono

, Kubo

, et al., Task Realization by a Force-Based Variable Compliance Controller for Flexible Motion Control Systems[J], IEEE Transactions on Industrial Electronics 61(2) (2013), 1009–1021.

Krainin

, Henry

, Ren

, et al., Manipulator and object tracking for in-hand 3D object modeling[J], International Journal of Robotics Research 30(11) (2011), 1311–1327.

Eck

, Kaas

A.L.

, Mulders

J.L.

, et al., The Effect of Task Instruction on Haptic Texture Processing: The Neural Underpinning of Roughness and Spatial Density Perception.[J], Cerebral Cortex 26(1) (2014), bhu294.

Peters

, Evaluation of Interactive Object Recognition with Tactile Sensing[J], Nucleic Acids Research (2015).

Alfadhel

and Kosel

, Magnetic Nanocomposite Cilia Tactile Sensor.[J], Advanced Materials 27(47) (2016), 7888–7892.

Wang

Y.C.

, Chen

T.Y.

, Chen

, et al., Mutual Capacitive Flexible Tactile Sensor for 3-D Image Control[J], Journal of Microelectromechanical Systems 22(3) (2013), 804–814.

Chen

, Wang

, Li

, et al., Flexible Piezoelectric-Induced Pressure Sensors for Static Measurements Based on Nanowires/Graphene Heterostructures.[J], Acs Nano 11(5) (2017), 4507.

Tao

, Zou

, Fei

, et al., From Dual-Mode Triboelectric Nanogenerator to Smart Tactile Sensor: A Multiplexing Design[J], Acs Nano 11(4) (2017), 3950.

Grzelczyk

, Stańczyk

and Awrejcewicz

, Prototype, control system architecture and controlling of the hexapod legs with nonlinear stick-slip vibrations[J], Mechatronics 37 (2016), 63–78.

10.

Jia , Yangqing , Shelhamer , et al., Caffe: Convolutional Architecture for Fast Feature Embedding[J], 2014.

11.

Lenz

, Lee

and Saxena

, Deep Learning for Detecting Robotic Grasps[J], International Journal of Robotics Research 34(4–5) (2014), 705–724.

12.

Moons

and Verhelst

, An Energy-Efficient Precision-Scalable ConvNet Processor in 40-nm CMOS[J], IEEE Journal of Solid-State Circuits 52(4) (2017), 903–914.

13.

Cheng

P.M.

and Malhi

H.S.

, Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images[J], Journal of Digital Imaging 30(2) (2017), 234.

14.

Limonchenko

I.Y.

, Massey products in cohomology of moment-angle manifolds for 2-truncated cubes[J], Russian Mathematical Surveys 71(2) (2016), 376–378.

15.

Chen

, Dou

, Yu

, et al., VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images[J], Neuroimage 170 (2017), S1053811917303348.

16.

Liu

, Li

, et al., Flexible dynamic measurement method of three-dimensional surface profilometry based on multiple vision sensors.[J], Optics Express 23(1) (2015), 384–400.

17.

Maier-Hein

, Mountney

, Bartoli

, et al., Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery.[J], Medical Image Analysis 17(8) (2013), 974–996.

18.

Gong

, Liu

and Zhang

, Flexible method of refraction correction in vision measurement systems with multiple glass ports[J], Optics Express 25(2) (2017), 831.

19.

Kelly

, Roy

and Sukhatme

G.S.

, Determining the Time Delay Between Inertial and Visual Sensor Measurements[J], IEEE Transactions on Robotics 30(6) (2017), 1514–1523.

20.

Rampp

, Barth

, Schülein

, et al., Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients.[J], IEEE Transactions on Biomedical Engineering 62(4) (2015), 1089–1097.

21.

Lakhal

, Melingui

and Merzouki

, Hybrid Approach for Modeling and Solving of Kinematics of a Compact Bionic Handling Assistant Manipulator[J], IEEE/ASME Transactions on Mechatronics 21(3) (2016), 1326–1335.

22.

Jiang

, Zhao

, Gao

, et al., Adaptive pseudo-color enhancement method of weld radiographic images based on HSI color space and self-transformation of pixels[J], Review of Scientific Instruments 88(6) (2017), 194.

23.

Sabour

, Frosst

and Hinton

G.E.

, Dynamic Routing Between Capsules[J], 2017.

Evolutionary bionic manipulator method based on capsule network

Abstract

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