Rao Priyanka, Peyron Quentin, Lilge Sven, Burgner-Kahrs Jessica
Continuum Robotics Laboratory, Department of Mathematical and Computational Sciences, University of Toronto Mississauga, Mississauga, ON, Canada.
Front Robot AI. 2021 Feb 2;7:630245. doi: 10.3389/frobt.2020.630245. eCollection 2020.
Tendon actuation is one of the most prominent actuation principles for continuum robots. To date, a wide variety of modelling approaches has been derived to describe the deformations of tendon-driven continuum robots. Motivated by the need for a comprehensive overview of existing methodologies, this work summarizes and outlines state-of-the-art modelling approaches. In particular, the most relevant models are classified based on backbone representations and kinematic as well as static assumptions. Numerical case studies are conducted to compare the performance of representative modelling approaches from the current state-of-the-art, considering varying robot parameters and scenarios. The approaches show different performances in terms of accuracy and computation time. Guidelines for the selection of the most suitable approach for given designs of tendon-driven continuum robots and applications are deduced from these results.
肌腱驱动是连续体机器人最突出的驱动原理之一。迄今为止，已经推导出了各种各样的建模方法来描述肌腱驱动的连续体机器人的变形。出于对现有方法进行全面概述的需要，这项工作总结并概述了当前的建模方法。特别是，最相关的模型是根据主干表示、运动学以及静态假设进行分类的。进行了数值案例研究，以比较当前最先进技术中代表性建模方法的性能，同时考虑不同的机器人参数和场景。这些方法在准确性和计算时间方面表现出不同的性能。从这些结果中推导出了针对给定设计的肌腱驱动连续体机器人和应用选择最合适方法的指导原则。
