ArticleThe International journal of robotics research2026
Configuration identification of on-demand variable stiffness strain-limiting layers in zig-zag soft pneumatic actuators using deep learning methods.
Article in The International journal of robotics research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Soft pneumatic actuators (SPAs) typically offer a fixed trajectory, resulting in one specific tip motion for a given range of pressure. When multiple trajectories are needed, these actuators require re-fabrication with altered structural designs, with different lengths, chamber sizes, and wall thicknesses etc. Passive modular variable stiffness SPAs present a significant advantage by enabling the realization of many distinct trajectories without structural redesign. Although various mathematical modeling techniques are widely used to predict their tip motion by treating it a kinematics problem, solving the inverse problem in the presence of modular strain-limiting layer (SLL) configurations is challenging. It is essential to determine the configuration of such a slender actuator in the form of a robot manipulator to deploy it for a specific function and application without re-fabricating them, by simply varying the SLL per the configuration required for a particular tip-point trajectory. To this aim, this paper introduces a hybrid methodology (based on feed-forward neural network) and a convolutional neural network-based method to predict the required SLL configuration for a particular tip trajectory of the SPA. This methodology is generic enough to apply to such actuators to predict their configuration as per their specific tip point trajectory in Cartesian space. The results presented for a slender SPA have demonstrated that the proposed method has predicted its configurations for a range of applications typified by an endoscope prototype, a soft robotic gripping application, and a system mimicking human finger movement with an average error of 1.65%. This study offers a versatile methodology for "function and application specific" SPAs or robot manipulators without re-fabricating them, by strategically combining SLL and machine learning-based prediction to generate a specific trajectory.
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