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TMTDyn: A Matlab Package for Modeling and Control of Hybrid Rigid-Continuum Robots Based on Discretized Lumped System and Reduced-Order Models

Research output: Contribution to journalArticle

Seyedmohammadhadi Sadati, S. Elnaz Naghibi, Ali Shiva, Brendan Michael, Ludovic Renson, Matthew Jacob William Howard, Caleb Rucker, Kaspar Althoefer, Thrisantha Nanayakkara, Steffen Zschaler, Christos Bergeles, Helmut Hauser, Ian D. Walker

Original languageEnglish
JournalINTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
Publication statusAccepted/In press - 6 Sep 2019

Documents

  • main_v5_0

    main_v5_0.pdf, 4.56 MB, application/pdf

    15/09/2019

    Accepted author manuscript

King's Authors

Abstract

A reliable, accurate, and yet simple dynamic model is important to analyze, design and control hybrid rigid-continuum robots.
Such models should be fast, as simple as possible and user-friendly to be widely accepted by the ever-growing robotics research community.
In this study, we introduce two new modeling methods for continuum manipulators: a general reduced-order model (ROM) and a discretized model with absolute states and Euler-Bernoulli beam segments (EBA).
Additionally, a new formulation is presented for a recently introduced discretized model based on Euler-Bernoulli beam segments and relative states (EBR).
We implement these models to a Matlab software package, named $TMTDyn$, to develop a modeling tool for hybrid rigid-continuum systems.
The package features a new High-Level Language (HLL) text-based interface, a CAD-file import module, automatic formation of the system Equation of Motion (EOM) for different modeling and control tasks, implementing Matlab C-mex functionality for improved performance, and modules for static and linear modal analysis of a hybrid system.
The underlying theory and software package are validated for modeling experimental results for (i) dynamics of a continuum appendage, and (ii) general deformation of a fabric sleeve worn by a rigid link pendulum.
A comparison shows higher simulation accuracy (8-14\% normalized error) and numerical robustness of the ROM model for a system with small number of states, and computational efficiency of the EBA model with near real-time performances that makes it suitable for large systems.
The challenges and necessary modules to further automate the design and analysis of hybrid systems with a large number of states are briefly discussed in the end.

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