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Analytical Differentiation of Manipulator Jacobian
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Publication Year
2020-11-01
Journal
Proceedings - 4th IEEE International Conference on Robotic Computing, IRC 2020
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - 4th IEEE International Conference on Robotic Computing, IRC 2020, pp.408-409
Keyword
Analytical differentiationJacobianKinematicsManipulator
Mesh Keyword
Analytical methodComputational costsControl performanceDesired trajectoriesIterative calculationNumerical derivationNumerical errorsReference signals
All Science Classification Codes (ASJC)
Computer Science ApplicationsArtificial IntelligenceAutomotive EngineeringControl and OptimizationSurgery
Abstract
In order to increase the control performance, many control algorithms utilize the acceleration information as a reference signal. An end-effector's velocity is mapped from joint velocity through multiplication with a Jacobian matrix. Therefore, in order to derive the joint acceleration corresponding to the desired trajectory of an end-effector, Jacobian differentiation should be calculated. Numerical methods for Jacobian differentiation are easy to implement and provide sufficiently accurate approximations. However, they incur high computational costs due to the iterative calculation of element-wise derivations. Also, numerical error is inevitable and it can no longer be ignored in near the singular point. Therefore, it is rather hard to compute control algorithm accurately in real-time. To resolve this problem, an analytical method for the differentiation is introduced in this paper. Since it does not need any approximation, it gives accurate result. In order to verify the effectiveness, the method is compared to the numerical derivation through computer simulations.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36600
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85099341956&origin=inward
DOI
https://doi.org/10.1109/irc.2020.00070
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9287817
Type
Conference
Funding
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT &Future Planning for convergent research in human plus (No, NRF-2019078037).
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Hong Young-Dae홍영대
Department of Electrical and Computer Engineering
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