A Static Modelling and Evaluation Framework for Soft Continuum Robots with Reinforced Chambers

Elastomer-based soft manipulators with fibre- reinforced chambers, represent a prevalent design paradigm in soft robotics. These robots incorporate multiple actuation chambers, enabling elongation and bending motions. However, the inherent compliance of materials and the pressurized chambers inevitably introduce significant nonlinearity to these robots. Moreover, design of such robots often relies on a trial-and-error approach. Consequently, a comprehensive robot prototyping framework is of paramount importance. To achieve this, we present a static modeling, design and evaluation framework for soft robots with densely reinforced chambers (i.e., the angle between the reinforcement fibre and the axial direction of soft robots is 90◦). We first propose a static analytical modeling framework to achieve both the forward kinematics and the tip force generation modeling. This modeling framework accommodates the effects of pressurized chambers and (non)linear material behaviors. Furthermore, our design and evaluation framework incorporates an open-accessible simulation toolbox with a user-friendly graphical interface, along with a physical evaluation platform. The entire framework is validated by eight kinds of manipulators with varying diameters and lengths. Meanwhile, the nonlinearity introduced by geometrical deformation resulting from the elongation, the pressurized actuation chambers (i.e., the chamber stiffening effect), and material hyperelasticity are investigated. Results also enable informed decision-making on design specifications prior to robot fabrication. Received 24 July 2025; accepted 14 October 2025. Date of publication 28 October 2025; date of current version 17 November 2025. This work was supported in part by the Springboard Award of the Academy of Medical Sciences under Grant SBF003-1109, in part by the Engineering and Physical Sciences Research Council under Grant EP/R037795/1, Grant EP/S014039/1, and Grant EP/V01062X/1, and in part by the UCL Dean’s Prize, and UCL Mechanical Engineering. This article was recommended for publication by Associate Editor F. Alambeigi and Editor H. Zhao upon evaluation of the reviewers’ comments. (Corresponding author: Helge A. Wurdemann.) Jialei Shi is with the Department of Mechanical Engineering, University College London, WC1E 6BT London, U.K., and also with the Hamlyn Centre for Robotic Surgery, Department of Mechanical Engineering, Imperial College London, SW7 2AZ London, U.K. (e-mail: j.shi@imperial.ac.uk). Hanyu Jin is with the Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15251-7525 USA (e-mail: hanyujin@ andrew.cmu.edu). Wenlong Gaozhang and Helge A. Wurdemann are with the Department of Mechanical Engineering, University College London, WC1E 6BT London, U.K. (e-mail: h.wurdemann@ucl.ac.uk). Ge Shi is with the Department of Mechanical Engineering, University Col- lege London, WC1E 6BT London, U.K., and also with Robotics and Au- tonomous Systems Group, CSIRO, Pullenvale, QLD 4069, Australia (e-mail: ge.shi@csiro.au). Sara-Adela Abad is with the Department of Mechanical Engineering, Univer- sity College London, WC1E 6BT London, U.K., and also with the Universidad Nacional de Loja, Loja 110103, Ecuador. This article has supplementary downloadable material available at https://doi.org/10.1109/TRO.2025.3626601, provided by the authors. Digital Object Identifier 10.1109/TRO.2025.3626601