Computational kinematics for robotic manipulators: instantaneous motion pattern
Óscar Salgado, Óscar Altuzarra, F. Viadero, A. Hernández
- Year
- 2010
- Citations
- 3
Abstract
Purpose The purpose of this paper is to provide a general approach to compute, determine, and characterize the connectivity of the end‐effector of a robotic manipulator of arbitrary architecture, in any of the postures that it can reach. Design/methodology/approach The types of motion of this link, i.e. translational, screw motions, combinations thereof, and self‐motions, are first defined and determined, simplifying the understanding of the instantaneous behaviour of the manipulator, aided by the definition of an alternative input basis. Findings The characterization provided by this paper simplifies the understanding of the instantaneous behaviour of the manipulator. The mobility of the end‐effector is completely characterized by the principal screws of its motion, which can be obtained from a generalized eigenproblem. In the process, alternative demonstrations of well‐known properties of the principal screws are provided. Research limitations/implications The approach presented is focused on the kinetostatic analysis of manipulators, and therefore, subjected to rigid body assumption. Practical implications This paper proposes effective approaches for engineering analysis of robotic manipulators. Originality/value This approach is based on a pure theoretical kinematic analysis that can characterize computationally the motion that the end‐effector of an industrial robot of general morphology (i.e. serial, parallel, hybrid manipulators, complex mechanisms, redundant or non‐redundantly actuated). Also, being implemented on a general‐purpose software for the kinematic analysis of mechanisms, it provides visual information of the motion capabilities of the manipulator, highly valuable on its design stages.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002