Force-position-workspace hybrid-based stability optimization of reconfigurable cable-driven parallel robot

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Cable-driven parallel robots (CDPRs) have been widely used as motion executers for their large workspace and lower inertia. However, there are few studies on structural optimization design considering its stability. This paper proposes a stability optimization method based on force-position workspace for a reconfigurable cable-driven parallel robot (RCDPR). First, the structural optimization analysis of RCDPR is carried out. Then, the forces distribution algorithm based on the feasibility of real-time control is determined, and the boundary contour algorithm (BCA) of the RCDPR force feasible workspace (FFW) on the central plane is proposed. Second, the stiffness and cables driving force space (CFS) models of RCDPR are established. Subsequently, the stability evaluation function is established to optimize the structure of RCDPR, which uses FFW and main task feasible workspace (MFW) as carriers and stiffness and CFS as weights. Finally, an experimental prototype of the developed robot is constructed, and motion performance and workspace verification experiments are conducted. The results demonstrate that the developed RCDPR has good motion accuracy and stable workspace, and the results also verify the feasibility of the stability evaluation function and BCA.

Original languageEnglish
Pages (from-to)2212-2245
Number of pages34
JournalRobotica
Volume43
Issue number6
DOIs
Publication statusPublished - 1 Jun 2025

Keywords

  • boundary contour algorithm
  • cable-driven parallel robot
  • cables driving force space
  • mechanical design
  • stability

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computational Mechanics
  • General Mathematics
  • Modelling and Simulation
  • Rehabilitation
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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