Shape Estimation of Soft Manipulator Using Stretchable Sensor.

Autor:	So J; Mechatronics R&D Center, Samsung Electronics, Republic of Korea., Kim U; Korea Institute of Machinery & Materials, Daejeon, Republic of Korea., Kim YB; AIDIN ROBOTICS Inc., Suwon, Republic of Korea., Seok DY; School of Mechanical Engineering, Sungkyunkwan University, Republic of Korea., Yang SY; School of Mechanical Engineering, Sungkyunkwan University, Republic of Korea., Kim K; School of Mechanical Engineering, Sungkyunkwan University, Republic of Korea., Park JH; School of Mechanical Engineering, Sungkyunkwan University, Republic of Korea., Hwang ST; School of Mechanical Engineering, Sungkyunkwan University, Republic of Korea., Gong YJ; School of Mechanical Engineering, Sungkyunkwan University, Republic of Korea., Choi HR; School of Mechanical Engineering, Sungkyunkwan University, Republic of Korea.
Jazyk:	angličtina
Zdroj:	Cyborg and bionic systems (Washington, D.C.) [Cyborg Bionic Syst] 2021 Apr 21; Vol. 2021, pp. 9843894. Date of Electronic Publication: 2021 Apr 21 (Print Publication: 2021).
DOI:	10.34133/2021/9843894
Abstrakt:	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. Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this article. (Copyright © 2021 Jinho So et al.)
Databáze:	MEDLINE
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