A novel class of reconfigurable parallel kinematic manipulators: Concepts and Fourier-based singularity analysis

Josue Camacho-Arreguin, Mingfeng Wang, Xin Dong, Dragos Axinte

Research output: Journal PublicationArticlepeer-review

11 Citations (Scopus)

Abstract

In-situ interventions in complex environments require new concepts of Parallel Kinematic Manipulators (PKMs) that present higher versatility for adapting to unstructured working environments without affecting their advantageous characteristics. To address this opportunity a novel class of adaptive PKMs is proposed, the upper joints are repositionable on the moving platform and the lower joints are free from a base. These characteristics give to the proposed PKMs the capability to modify its workspace and performance (i.e. stiffness and singularity avoidance) to work on non-conventional manufacturing/repair environments. Further, the kinematic and workspace models of the proposed PKMs are introduced. More importantly, a unique method for singularity analysis is introduced, as the versatility to modify the workspace comes with the disadvantage of singularities that are not constant. To address this issue a strategy based on the Fourier transform is introduced, the dominant frequencies are identified and configurations with lower dominant frequencies are weighted to stabilise the Jacobian of the parallel mechanism. Finally, a set of validations are presented for proving the proposed method of singularity analysis by FEA with an error smaller than 1%.

Original languageEnglish
Article number103993
JournalMechanism and Machine Theory
Volume153
DOIs
Publication statusPublished - Nov 2020

Keywords

  • Fourier analysis
  • Reconfigurable parallel kinematic manipulators
  • Singularity analysis

ASJC Scopus subject areas

  • Bioengineering
  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'A novel class of reconfigurable parallel kinematic manipulators: Concepts and Fourier-based singularity analysis'. Together they form a unique fingerprint.

Cite this