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Oziel Rios

dblp:175/5637 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot kinematics
kinematic modeling
0.212016
Method of Influence Coefficients for Kinematic and Dynamic Modeling of Robotic Systems · IEEE Trans. Robotics 2016
Robotics › Motion planning and robot control › robot dynamics
robot dynamics modeling
0.212016
Method of Influence Coefficients for Kinematic and Dynamic Modeling of Robotic Systems · IEEE Trans. Robotics 2016

Methods — techniques the papers use, named apart from their topics

generalized coordinates · 0.2algebraic transformation · 0.2
YearPublicationVenuePosition
2016 Method of Influence Coefficients for Kinematic and Dynamic Modeling of Robotic Systems
abstract
In this paper, a summary of the method of influence coefficients for kinematic and dynamic modeling is presented and extended to kinematic trees. This method allows the decoupling of the parameters defining the system's geometry and mass properties from the time states of the generalized coordinates used to define the system, which further allows for the kinematic and dynamic models to be transformed from one set of coordinates to another in a purely algebraic manner. For a multi-input–multi-output system such as a planar or spatial robotic system, the first-order influence coefficients are matrices, while the second-order influence coefficients are arrays of matrices. This paper starts by describing the structure of these coefficients followed by a description of the algebraic operations involving these. Next, an approach to compute the kinematic influence coefficients for kinematic trees of general geometry is presented followed by a description of the computations involved in determining the inertial influence coefficients used to compute the inertial forces acting on the system. The transformation of the influence coefficients from one set of generalized coordinates to another is also shown. Finally, a planar mechanism with seven joints and two end-effectors is used to illustrate the use of the method and to convert an open-loop mechanism into a closed-loop one.
Oziel Rios
IEEE Trans. Robotics1