Sadati S M H, Naghibi S Elnaz, da Cruz Lyndon, Bergeles Christos
Robotics and Vision in Medicine (RViM) Lab, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United kingdom.
Department of Aeronautics, Faculty of Engineering, Imperial College London, London, England, United kingdom.
Front Robot AI. 2023 Sep 15;10:1094114. doi: 10.3389/frobt.2023.1094114. eCollection 2023.
Soft robot's natural dynamics calls for the development of tailored modeling techniques for control. However, the high-dimensional configuration space of the geometrically exact modeling approaches for soft robots, i.e., Cosserat rod and Finite Element Methods (FEM), has been identified as a key obstacle in controller design. To address this challenge, Reduced Order Modeling (ROM), i.e., the approximation of the full-order models, and Model Order Reduction (MOR), i.e., reducing the state space dimension of a high fidelity FEM-based model, are enjoying extensive research. Although both techniques serve a similar purpose and their terms have been used interchangeably in the literature, they are different in their assumptions and implementation. This review paper provides the first in-depth survey of ROM and MOR techniques in the continuum and soft robotics landscape to aid Soft Robotics researchers in selecting computationally efficient models for their specific tasks.
软机器人的自然动力学特性要求开发定制的建模技术用于控制。然而,软机器人几何精确建模方法(即柯塞尔梁和有限元方法(FEM))的高维配置空间已被视为控制器设计中的关键障碍。为应对这一挑战,降阶建模(ROM),即全阶模型的近似,以及模型阶数缩减(MOR),即降低基于高保真有限元模型的状态空间维度,正受到广泛研究。尽管这两种技术目的相似且其术语在文献中常被互换使用,但它们在假设和实现方面存在差异。这篇综述文章首次对连续体和软机器人领域中的ROM和MOR技术进行了深入调查,以帮助软机器人研究人员为其特定任务选择计算效率高的模型。