Optimal configuration of a parallel kinematic manipulator for the maximum dynamic load-carrying capacity

Chun Ta Chen*, Shi Chang Zeng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In determining the maximum dynamic loadcarrying capacity (DLCC) of reconfigurable motor-driven parallel kinematic manipulators (PKM), the objective is to identify the optimal configuration which accomplishes the assigned motion for the maximum DLCC subject to the constraints imposed by the kinematics and dynamics of the manipulator structure. In this study, the maximum DLCC problem of a reconfigurable PKM is formulated using the structured Boltzmann-Hamel-d- Alembert formulism, and then the optimal reconfiguration is obtained using a two-loop of optimization process, in which the particle swarm optimization (PSO) algorithm is for the outer-loop optimization and the linear programming (LP) method is for the inner-loop optimization, such that the reconfiguration is achieved by relocating the base points along linear guideways. The numerical results present the effects of the base locations on the DLCC and the corresponding kinematics and dynamics along the prescribed trajectory.

Original languageEnglish
Title of host publicationProceedings - 1st International Conference on Robot, Vision and Signal Processing, RVSP 2011
Pages122-125
Number of pages4
DOIs
Publication statusPublished - 2011
Event1st International Conference on Robot, Vision and Signal Processing, RVSP 2011 - Kaohsiung, Taiwan
Duration: 2011 Nov 212011 Nov 23

Publication series

NameProceedings - 1st International Conference on Robot, Vision and Signal Processing, RVSP 2011

Other

Other1st International Conference on Robot, Vision and Signal Processing, RVSP 2011
Country/TerritoryTaiwan
CityKaohsiung
Period2011/11/212011/11/23

Keywords

  • Boltzmann-Hamel-d-Alembert
  • Dynamic load-carrying capacity
  • Parallel kinematic manipulator
  • Particle swarm optimization
  • Reconfigurable

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

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