Static Force-Based Modeling and Parameter Estimation for a Deformable Link Composed of Passive Spherical Joints With Preload Forces

Gaofeng Li; Dezhen Song; Lei Sun; Shan Xu; Hongpeng Wang; Jingtai Liu

doi:10.1109/JAS.2019.1911549

Volume 8 Issue 11

Nov. 2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2021 > 8(11): 1817-1826

G. F. Li, D. Z. Song, L. Sun, S. Xu, H. P. Wang, and J. T. Liu, "Static Force-Based Modeling and Parameter Estimation for a Deformable Link Composed of Passive Spherical Joints With Preload Forces," IEEE/CAA J. Autom. Sinica, vol. 8, no. 11, pp. 1817-1826, Nov. 2021. doi: 10.1109/JAS.2019.1911549

Citation:

G. F. Li, D. Z. Song, L. Sun, S. Xu, H. P. Wang, and J. T. Liu, "Static Force-Based Modeling and Parameter Estimation for a Deformable Link Composed of Passive Spherical Joints With Preload Forces," IEEE/CAA J. Autom. Sinica, vol. 8, no. 11, pp. 1817-1826, Nov. 2021. doi: 10.1109/JAS.2019.1911549

Citation:

G. F. Li, D. Z. Song, L. Sun, S. Xu, H. P. Wang, and J. T. Liu, "Static Force-Based Modeling and Parameter Estimation for a Deformable Link Composed of Passive Spherical Joints With Preload Forces," IEEE/CAA J. Autom. Sinica, vol. 8, no. 11, pp. 1817-1826, Nov. 2021. doi: 10.1109/JAS.2019.1911549

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Static Force-Based Modeling and Parameter Estimation for a Deformable Link Composed of Passive Spherical Joints With Preload Forces

doi: 10.1109/JAS.2019.1911549

Gaofeng Li^{1, 2
,},
Dezhen Song^3
,,
Lei Sun^{4, 5
,},
Shan Xu^{4, 5
,},
Hongpeng Wang^{4, 5
,},
Jingtai Liu^{4, 5
,
,}

1.
Nankai University, Tianjin 300350, China
2.
Humanoids and Human Centered Mechatronics Research Line, Italian Institute of Technology, 16163 Genova, Italy
3.
Department of Computer Science and Engineering, Texas A&M University, College Station, Texas 77843 USA
4.
Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300350, China
5.
Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China

Funds: This work was supported by National Natural Science Foundation of China (NSFC) (61573198, 61375087), and the Natural Science Foundation of Tianjin (15JCZDJC31200)

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Author Bio:
Gaofeng Li received the B.E. and Ph.D. degrees from Nankai University in 2013 and 2018, respectively. He was a Visiting Student with Netbot Lab., Texas A&M University, College Station, USA, from November 2015 to December 2016. He is currently a Postdoctoral Researcher with the Italian Institute of Technology, Italy. His research interests include model-less motion control, lie group in robotics, haptic teleoperation, and soft robotics. Dr. Li was the Recipient of the Outstanding Doctoral Dissertation of Nankai University in 2019. He is also a Member of the IEEE Robotics and Automation Society and an Independent Reviewer for the IEEE Transactions on Automation Science and Engineering, IEEE/ASME Transactions on Mechatronics, IEEE Robotic and Automation Letters, IEEE International Conference on Robotics and Automation, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE International Conference on Automation Science and Engineering, etc

Dezhen Song received the B.S. and M.S. degrees from Zhejiang University in 1995 and 1998, respectively, and the Ph.D. degree from the University of California, USA, in 2004. He is currently a Professor with Texas A&M University, College Station, USA. His primary research interests include networked robotics, distributed sensing, computer vision, surveillance, and stochastic modeling. Dr. Song received the Kayamori Best Paper Award of the 2005 IEEE International Conference on Robotics and Automation (with J. Yi and S. Ding). He received the NSF Faculty Early Career Development (CAREER) Award in 2007. From 2008 to 2012, he was an Associate Editor of the IEEE Transactions on Robotics. From 2010 to 2014, he was an Associate Editor of the IEEE Transactions on Automation Science and Engineering

Lei Sun received the B.E. and M.E. degrees in mechatronics engineering from Tianjin University in 1999 and 2002, respectively, and the Ph.D. degree in control theory and control engineering from Nankai University in 2005. In 2005, he joined Nankai University. He is currently an Associate Professor at the College of Artificial Intelligence, Nankai University. His research interests include robotics and automatic control, teleoperation of robots in networks, wireless sensor network, and micro-electromechanical systems

Shan Xu received the B.E. degree in intelligent science and technology and the M.E. degree in control science and engineering from Nankai University in 2016 and 2019, respectively. She is currently working toward the Ph.D. degree in systems engineering with the University of Virginia, Charlottesville, USA. She is currently focusing on the human perception of touch for haptics display and human-machine interaction. Her research interests include robot kinematics, screw theory, trajectory planning, 3-D tracking, and haptics

Hongpeng Wang received the B.S degree in physics and the Ph.D degree in control theory and engineering from Nankai University in 2000 and 2009, respectively. He is currently an Associate Professor with Nankai University. His primary research interests include cloud and networked robotics, aerial and wheeled robotics systems, cyber-physical network systems, and ecological tele-observation. During Sept. 2007–Sept. 2008, Dec. 2014–Feb. 2015, he was a Visiting Research Fellow with the Department of Computer Science and Engineering, Texas A&M University, College Station. During Mar. 2016–Mar. 2017, he was a Visiting Scholar and Post Doc. with the Department of Computer Science and Engineering, Texas A&M University, College Station. Dr. Wang is a Member of the IEEE Robotics and Automation Society

Jingtai Liu received the B.S. degree in engineering of industry automation from Tianjin University in 1983, the M.Eng. degree in electrical engineering from Tianjin University in 1986, and the Ph.D. degree in robotics engineering from Nankai University in 1998. He is currently the Director of the Institute of Robotics and Automatic Information System, Nankai University, where he is also a Professor with the College of Artificial Intelligence. His current research interests include robotics and automatic control, competitive networked robots, wireless sensor networks, automatic information systems, and robotics education for kids. During 2006–2007, he was a Visiting Scholar with the Laboratory for Automation Science and Engineering, University of California, USA. Dr. Liu was a Member of the Industry Robotics Technology Expert Group, which was a High-tech Research and Development Program of China (China 863 Program), during 1997–2001
Corresponding author: Jingtai Liu, e-mail: liujt@nankai.edu.cn
¹ The material is polyoxymethylene.
Received Date: 2017-10-01
Revised Date: 2017-12-30
Accepted Date: 2018-05-17

Available Online: 2019-05-17

Abstract

Abstract

To balance the contradiction between higher flexibility and heavier load bearing capacity, we present a novel deformable manipulator which is composed of active rigid joints and deformable links. The deformable link is composed of passive spherical joints with preload forces between socket-ball surfaces. To estimate the load bearing capacity of a deformable link, we present a static force-based model of spherical joint with preload force and analyze the static force propagation in the deformable link. This yields an important result that the load bearing capacity of a spherical joint only depends on its radius, preload force, and static friction coefficient. We further develop a parameter estimation method to estimate the product of preload force and static friction coefficient. The experimental results validate our model. 80.4% of percentage errors on the maximum payload mass prediction are below 15%.
- Deformable link,
- parameters identification,
- spherical joint with preload force,
- static force-based model

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¹ The material is polyoxymethylene.

References(28)

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To balance the contradiction between flexibility and load bearing capacity, we introduce a deformable link composed of series of spherical joints
We present a static force-based model of spherical joints with preload forces and analyze the static force propagation in the deformable link
We proposed a low-cost calibration method to identify the model parameters

Static Force-Based Modeling and Parameter Estimation for a Deformable Link Composed of Passive Spherical Joints With Preload Forces

doi: 10.1109/JAS.2019.1911549

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通讯作者: 陈斌, bchen63@163.com

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