FBG-Based Control of a Continuum Manipulator Interacting With Obstacles

被引:0
作者
Sefati, Shahriar [1 ,2 ,3 ]
Murphy, Ryan J. [4 ,5 ]
Alambeigi, Farshid [1 ,2 ]
Pozin, Michael [2 ]
Iordachita, Iulian [1 ,2 ]
Taylor, Russell H. [1 ,3 ]
Armand, Mehran [1 ,2 ,4 ]
机构
[1] Johns Hopkins Univ, Lab Computat Sensing & Robot, Baltimore, MD 21218 USA
[2] Johns Hopkins Univ, Dept Mech Engn, Baltimore, MD 21218 USA
[3] Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD 21218 USA
[4] Johns Hopkins Univ, Appl Phys Lab, Laurel, MD USA
[5] Auris Hlth Inc, Redwood City, CA USA
来源
2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) | 2018年
关键词
DESIGN;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Tracking and controlling the shape of continuum dexterous manipulators (CDM) in constraint environments is a challenging task. The imposed constraints and interaction with unknown obstacles may conform the CDM's shape and therefore demands for shape sensing methods which do not rely on direct line of sight. To address these issues, we integrate a novel Fiber Bragg Grating (FBG) shape sensing unit into a CDM, reconstruct the shape in real-time, and develop an optimization-based control algorithm using FBG tip position feedback. The CDM is designed for less-invasive treatment of osteolysis (bone degradation). To evaluate the performance of the feedback control algorithm when the CDM interacts with obstacles, we perform a set of experiments similar to the real scenario of the CDM interaction with soft and hard lesions during the treatment of osteolysis. In addition, we propose methods for identification of the CDM collisions with soft or hard obstacles using the jacobian information. Results demonstrate successful control of the CDM tip based on the FBG feedback and indicate repeatability and robustness of the proposed method when interacting with unknown obstacles.
引用
收藏
页码:6477 / 6483
页数:7
相关论文
共 50 条
[41]   AeCoM: An Aerial Continuum Manipulator With IMU-Based Kinematic Modeling and Tendon-Slacking Prevention [J].
Peng, Rui ;
Wang, Zehao ;
Lu, Peng .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2023, 53 (08) :4740-4752
[42]   Adaptive Fuzzy Backstepping Control Based on Dynamic Surface Control for Uncertain Robotic Manipulator [J].
Zhou, Jinglei ;
Liu, Endong ;
Tian, Xiumei ;
Li, Zhenwu .
IEEE ACCESS, 2022, 10 :23333-23341
[43]   Nonlinear Lyapounov based control of an underwater vehicle in presence of uncertainties and obstacles [J].
Khalaji, Ali Keymasi ;
Tourajizadeh, Hami .
OCEAN ENGINEERING, 2020, 198
[44]   Discrete Cosserat Static Model-Based Control of Soft Manipulator [J].
Li, Haihong ;
Xun, Lingxiao ;
Zheng, Gang ;
Renda, Federico .
IEEE ROBOTICS AND AUTOMATION LETTERS, 2023, 8 (03) :1739-1746
[45]   Tracking Control of Manipulator Based on High-Order Disturbance Observer [J].
Zheng, Wenchao ;
Chen, Mou .
IEEE ACCESS, 2018, 6 :26753-26764
[46]   Observer-Based Robust Adaptive Control of Uncertain Robot Manipulator [J].
Mohammed, Abdul Rehan Khan ;
Zhang, Jiayi ;
Bilal, Ahmad .
2022 IEEE 5TH INTERNATIONAL CONFERENCE ON INDUSTRIAL CYBER-PHYSICAL SYSTEMS, ICPS, 2022,
[47]   Novel sliding mode control of the manipulator based on a nonlinear disturbance observer [J].
Guo, Keyou ;
Zhang, Haoze ;
Wei, Caili ;
Jiang, Haibing ;
Wang, Jiangnan .
SCIENTIFIC REPORTS, 2024, 14 (01)
[48]   A novel aerial manipulator system compensation control based on ADRC and backstepping [J].
Ma, Le ;
Yan, Yiming ;
Li, Zhiwei ;
Liu, Jie .
SCIENTIFIC REPORTS, 2021, 11 (01)
[49]   Boundary control for a flexible manipulator based on infinite dimensional disturbance observer [J].
Jiang, Tingting ;
Liu, Jinkun ;
He, Wei .
JOURNAL OF SOUND AND VIBRATION, 2015, 348 :1-14
[50]   RBF Neural Network Based Backstepping Control for an Electrohydraulic Elastic Manipulator [J].
Duc-Thien Tran ;
Minh-Nhat Nguyen ;
Ahn, Kyoung Kwan .
APPLIED SCIENCES-BASEL, 2019, 9 (11)