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. 2021 Apr 21:2021:9843894.
doi: 10.34133/2021/9843894. eCollection 2021.

Shape Estimation of Soft Manipulator Using Stretchable Sensor

Jinho So  1 , Uikyum Kim  2 , Yong Bum Kim  3 , Dong-Yeop Seok  4 , Sang Yul Yang  4 , Kihyeon Kim  4 , Jae Hyeong Park  4 , Seong Tak Hwang  4 , Young Jin Gong  4 , Hyouk Ryeol Choi  4
Affiliations

Shape Estimation of Soft Manipulator Using Stretchable Sensor

Jinho So et al. Cyborg Bionic Syst. .

Abstract

The soft robot manipulator is attracting attention in the surgical fields with its intrinsic softness, lightness in its weight, and safety toward the human organ. However, it cannot be used widely because of its difficulty of control. To control a soft robot manipulator accurately, shape sensing is essential. This paper presents a method of estimating the shape of a soft robot manipulator by using a skin-type stretchable sensor composed of a multiwalled carbon nanotube (MWCNT) and silicone (p7670). The sensor can be easily fabricated and applied by simply attaching it to the surface of the soft manipulator. In its fabrication, MWCNT is sprayed on a teflon sheet, and liquid-state silicone is poured on it. After curing, we turn it over and cover it with another silicone layer. The sensor is fabricated with a sandwich structure to decrease the hysteresis of the sensor. After calibration and determining the relationship between the resistance of the sensor and the strain, three sensors are attached at 120° intervals. Using the obtained data, the curvature of the manipulator is calculated, and the entire shape is reconstructed. To validate its accuracy, the estimated shape is compared with the camera data. We experiment with three, six, and nine sensors attached, and the result of the error of shape estimation is compared. As a result, the minimum tip position error is approximately 8.9 mm, which corresponded to 4.45% of the total length of the manipulator when using nine sensors.

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Conflict of interest statement

The authors declare that there are no conflicts of interest regarding the publication of this article.

Figures

Figure 1
Figure 1
Concept design of shape estimation using stretchable strain sensor. Curvature data can be calculated from attached sensors, and the shape of the soft manipulator can be found.
Figure 2
Figure 2
Schematic top view of silicone tube with 3 sensors. Each sensor was attached at 120° intervals. The direction of bending can be achieved from each sensor data.
Figure 3
Figure 3
Flowchart of the whole shape estimation process.
Figure 4
Figure 4
Stretchable strain sensor fabrication process with p7670 and MWCNT.
Figure 5
Figure 5
Design parameter of stretchable strain sensor for shape estimation.
Figure 6
Figure 6
Sensor attachment method: (a) attachment of one set of sensors. One set consists of 3 sensors. Each sensor is spaced 120 degrees apart. (b) Silicone tube with 3 sets of sensors.
Figure 7
Figure 7
(a) Sensor calibration zig. Stretchable strain sensor with (b) 0% strain and (c) 20% strain.
Figure 8
Figure 8
Experiment results of sensor performance: (a) response of 30% strain/release cycles; (b) response of 1000 times strain/release cycles; (c) response of single-step strain; (d) response of multistep strain; (e) frequency response of the sensor; (f) impedance and phase angle.
Figure 9
Figure 9
Prestrained sensor data of both sides when strain profile is applied (a). Fitted graph (b).
Figure 10
Figure 10
Comparison between estimated shape and camera data with various deformations (a–d).
See this image and copyright information in PMC

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