Reconstruction method with twisting measurement and compensation for shape sensing of flexible robots

doi:10.1007/s40436-023-00469-7

Advances in Manufacturing ›› 2025, Vol. 13 ›› Issue (4): 886-900.doi: 10.1007/s40436-023-00469-7

• • 上一篇

Reconstruction method with twisting measurement and compensation for shape sensing of flexible robots

Xiang-Yan Chen, Ting-Ting Shen, Jin-Wu Qian, Ying-Jie Yu, Zhong-Hua Miao

School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, People's Republic of China

收稿日期:2023-01-18 修回日期:2023-05-19 发布日期:2025-12-06
通讯作者: Xiang-Yan Chen Email:E-mail:chxy0704@163.com E-mail:chxy0704@163.com
作者简介:Xiang-Yan Chen received the Ph.D. Degree in mechanical engineering from Shanghai University, Shanghai, China, in 2020. She is currently engaged in post-doctoral research at Shanghai University. Her research interests include shape sensing with fiber bragg grating and flexible robots.
Ting-Ting Shen received the M.D. degree in mechanical engineering from Shanghai University, Shanghai, China, in 2022. Her research interests include shape reconstruction with FBG sensors.
Jin-Wu Qian is a Professor at School of Mechatronics Engineering and Automation, Shanghai University. He received the Ph.D. from Beijing University of Aeronautics and Astronautics in1994. His current research interests include advanced robot, digital medical equipment and endoscopic visualization technology.
Ying-Jie Yu is a Professor at School of Mechatronics Engineering and Automation, Shanghai University. She received the Ph.D. from the Harbin Institute of Technology. Her current research interests include precision optical-testing technologies, instruments, computational imaging technologies, and applications.
Zhong-Hua Miao is a Professor at School of Mechatronics Engineering and Automation, Shanghai University. He received the Ph.D. from Shanghai Jiao Tong University. His research interests include intelligent equipment and robot-control technologies.
基金资助:
This study was supported by the National Natural Science Foundation of China (Grant No. 52075314).

Reconstruction method with twisting measurement and compensation for shape sensing of flexible robots

Xiang-Yan Chen, Ting-Ting Shen, Jin-Wu Qian, Ying-Jie Yu, Zhong-Hua Miao

School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, People's Republic of China

Received:2023-01-18 Revised:2023-05-19 Published:2025-12-06
Contact: Xiang-Yan Chen Email:E-mail:chxy0704@163.com E-mail:chxy0704@163.com
Supported by:
This study was supported by the National Natural Science Foundation of China (Grant No. 52075314).

摘要/Abstract

摘要： Flexible robots can reach a target treatment part with a complex shape and zigzagging path in a limited space owing to the advantages of a highly flexible structure and high accuracy. Thus, research of the shape detection of flexible robots is important. A reconstruction method including torsion compensation is proposed, then the method with a numerical method that does not include torsion compensation is compared. The microsegment arc between two adjacent measurement points is regarded as an arc in a close plane and a circular helix in three-dimensional (3D) space during the shape reconstruction process. The simulation results show that the two algorithms perform equally well regarding 2D curves. For the 3D curves, the Frenet-based reconstruction method with torsion compensation produced a higher fitting accuracy compared with the numerical method. For the microsegment arc lengths of 40 mm and 20 mm, the maximum relative errors were reduced by 11.3% and 20.1%, respectively, for the 3D curves when the reconstruction method based on Frenet with twisting compensation was used. The lengths of the packaging grid points were 40 mm and 20 mm, and the sensing length was 260 mm for the no-substrate sensor. In addition, a shape reconstruction experiment was performed, and the shape reconstruction accuracies of the sensors were 2.817% and 1.982%.

The full text can be downloaded at https://doi.org/10.1007/s40436-023-00469-7

关键词: Flexible robots, Reconstruction method, Twisting compensation, Frenet-Serret, Shape reconstruction

Abstract: Flexible robots can reach a target treatment part with a complex shape and zigzagging path in a limited space owing to the advantages of a highly flexible structure and high accuracy. Thus, research of the shape detection of flexible robots is important. A reconstruction method including torsion compensation is proposed, then the method with a numerical method that does not include torsion compensation is compared. The microsegment arc between two adjacent measurement points is regarded as an arc in a close plane and a circular helix in three-dimensional (3D) space during the shape reconstruction process. The simulation results show that the two algorithms perform equally well regarding 2D curves. For the 3D curves, the Frenet-based reconstruction method with torsion compensation produced a higher fitting accuracy compared with the numerical method. For the microsegment arc lengths of 40 mm and 20 mm, the maximum relative errors were reduced by 11.3% and 20.1%, respectively, for the 3D curves when the reconstruction method based on Frenet with twisting compensation was used. The lengths of the packaging grid points were 40 mm and 20 mm, and the sensing length was 260 mm for the no-substrate sensor. In addition, a shape reconstruction experiment was performed, and the shape reconstruction accuracies of the sensors were 2.817% and 1.982%.

The full text can be downloaded at https://doi.org/10.1007/s40436-023-00469-7

Key words: Flexible robots, Reconstruction method, Twisting compensation, Frenet-Serret, Shape reconstruction

Xiang-Yan Chen, Ting-Ting Shen, Jin-Wu Qian, Ying-Jie Yu, Zhong-Hua Miao. Reconstruction method with twisting measurement and compensation for shape sensing of flexible robots[J]. Advances in Manufacturing, 2025, 13(4): 886-900.

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Reconstruction method with twisting measurement and compensation for shape sensing of flexible robots

Reconstruction method with twisting measurement and compensation for shape sensing of flexible robots

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