A computational framework for the dynamic analyses of cable-driven parallel robots with feed and retrieval of cables

Accurate simulations of the motion of cable-driven parallel robots (CDPRs) are helpful in its modal analysis, testing robust control strategies, validating designs and estimating workspaces. On that account, a modular and computationally efficient framework to analyse the dynamics of CDPRs is develo...

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Bibliographic Details
Published in:Mechanism and machine theory Vol. 186; p. 105338
Main Authors: Mamidi, Teja Krishna, Bandyopadhyay, Sandipan
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.08.2023
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ISSN:0094-114X, 1873-3999
Online Access:Get full text
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Summary:Accurate simulations of the motion of cable-driven parallel robots (CDPRs) are helpful in its modal analysis, testing robust control strategies, validating designs and estimating workspaces. On that account, a modular and computationally efficient framework to analyse the dynamics of CDPRs is developed in the present work. In contrast to the prior studies, the inertia, stiffness and damping properties of the cables, along with their temporal variations caused by feeding and retrieving, are included in the dynamic model. Further, the forward dynamics algorithm designed for this purpose is recursive in nature and has linear time complexity. Finally, the efficacy of the proposed framework is established with the help of the FAST manipulator, the largest existing CDPR. Also, its extensive application potential is established via the study of the CDPR CoGiRo, which, apart from being actuated redundantly, differs drastically from the former in terms of its mass and footprint. •Dynamics of Type I cable-driven parallel robots (CDPRs) are analysed.•Cables are modelled via MRFEs, including their feeds and retrievals.•A modified RSSLM forward dynamic algorithm is employed for computational efficiency.•Examples of FAST and CoGiRo are used, with time-varying inertia, elasticity and damping.
ISSN:0094-114X
1873-3999
DOI:10.1016/j.mechmachtheory.2023.105338