Details
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 |
| Pages | 5125-5132 |
| Number of pages | 8 |
| ISBN (electronic) | 9781538680940 |
| Publication status | Published - 27 Dec 2018 |
Publication series
| Name | IEEE International Conference on Intelligent Robots and Systems |
|---|---|
| ISSN (Print) | 2153-0858 |
| ISSN (electronic) | 2153-0866 |
Abstract
Keywords
- actuators, approximation theory, neural nets, position control, robot kinematics, forward kinematics, concentric tube continuum robot, high computational load, nonlinear modeling effort, data-driven approach, physics-based model, robot prototype, inverse kinematics approximation, artificial neural network, ReLU, rectified linear unit, rotation actuator error, trigonometric function, mechanics modeling, Electron tubes, Robots, Kinematics, Neural networks, Quaternions, Computational modeling, Load modeling
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Computer Science Applications
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2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018. 2018. p. 5125-5132 8594451 (IEEE International Conference on Intelligent Robots and Systems).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Learning the Forward and Inverse Kinematics of a 6-DOF Concentric Tube Continuum Robot in SE(3)
AU - Grassmann, R.
AU - Modes, V.
AU - Burgner-Kahrs, J.
N1 - Publisher Copyright: © 2018 IEEE. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/12/27
Y1 - 2018/12/27
N2 - Recent physics-based models of concentric tube continuum robots are able to describe pose of the tip, given the preformed translation and rotation in joint space of the robot. However, such model-based approaches are associated with high computational load and highly non-linear modeling effort. A data-driven approach for computationally fast estimation of the kinematics without requiring the knowledge and the uncertainties in the physics-based model would be an asset. This paper introduces an approach to solve the forward kinematics as well as the inverse kinematics of concentric tube continuum robots with 6-DOF in three dimensional space SE(3). Two artificial neural networks with ReLU (rectified linear unit) activation functions are designed in order to approximate the respective kinematics. Measured data from a robot prototype are used in order to train, validate, and test the proposed approach. We introduce a representation of the rotatory joints by trigonometric functions that improves the accuracy of the approximation. The results with experimental measurements show higher accuracy for the forward kinematics compared to the state of the art mechanics modeling. The tip error is less then 2.3 mm w.r.t. position (1 % of total robot length) and 1.1° w.r.t. orientation. The single artificial neural network for the inverse kinematics approximation achieves a translation and rotation actuator error of 4.0 mm and 8.3 0, respectively.
AB - Recent physics-based models of concentric tube continuum robots are able to describe pose of the tip, given the preformed translation and rotation in joint space of the robot. However, such model-based approaches are associated with high computational load and highly non-linear modeling effort. A data-driven approach for computationally fast estimation of the kinematics without requiring the knowledge and the uncertainties in the physics-based model would be an asset. This paper introduces an approach to solve the forward kinematics as well as the inverse kinematics of concentric tube continuum robots with 6-DOF in three dimensional space SE(3). Two artificial neural networks with ReLU (rectified linear unit) activation functions are designed in order to approximate the respective kinematics. Measured data from a robot prototype are used in order to train, validate, and test the proposed approach. We introduce a representation of the rotatory joints by trigonometric functions that improves the accuracy of the approximation. The results with experimental measurements show higher accuracy for the forward kinematics compared to the state of the art mechanics modeling. The tip error is less then 2.3 mm w.r.t. position (1 % of total robot length) and 1.1° w.r.t. orientation. The single artificial neural network for the inverse kinematics approximation achieves a translation and rotation actuator error of 4.0 mm and 8.3 0, respectively.
KW - actuators
KW - approximation theory
KW - neural nets
KW - position control
KW - robot kinematics
KW - forward kinematics
KW - concentric tube continuum robot
KW - high computational load
KW - nonlinear modeling effort
KW - data-driven approach
KW - physics-based model
KW - robot prototype
KW - inverse kinematics approximation
KW - artificial neural network
KW - ReLU
KW - rectified linear unit
KW - rotation actuator error
KW - trigonometric function
KW - mechanics modeling
KW - Electron tubes
KW - Robots
KW - Kinematics
KW - Neural networks
KW - Quaternions
KW - Computational modeling
KW - Load modeling
UR - http://www.scopus.com/inward/record.url?scp=85062986293&partnerID=8YFLogxK
U2 - 10.1109/iros.2018.8594451
DO - 10.1109/iros.2018.8594451
M3 - Conference contribution
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 5125
EP - 5132
BT - 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
ER -