Modeling of a two-degree-of-freedom fiber-reinforced soft pneumatic actuator

Ferrandy, Varell; Indrawanto,; Ferryanto, F.; Sugiharto, Arif; Franco, Enrico; Garriga-Casanovas, Arnau; Mahyuddin, Andi Isra; Rodriguez y Baena, Ferdinando; Mihradi, Sandro; Virdyawan, Vani

doi:10.1017/s0263574723001170

Cited by 7 publications

(1 citation statement)

References 22 publications

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“…This last factor makes it difficult to create an accurate robot model and typical models contain significant uncertainty. Accurate finite element models have been developed [9], but even small variations in the manufacturing process due to practical tolerances lead to significant variations in robot behavior. Despite these uncertainties, model-based closed-loop dynamic control has been investigated by these authors [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Control of Soft Robotic Manipulator

Garriga-Casanovas,

Shakib,

Ferrandy

et al. 2024

Preprint

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Soft robotic manipulators consisting of serially-stacked segments combine actuation and structure in an integrated design. This design can be miniaturised while providing suitable actuation for applications such as minimally invasive surgery and for inspections in confined environments. The control of these robots, however, remains challenging, due to the difficulty in accurately modelling the robots, in coping with their redundancies, and in solving their full inverse kinematics. In this work, we explore a hybrid approach to control serial soft robotic manipulators that combines machine learning (ML) to estimate the inverse kinematics with a closed-loop control to compensate the remaining errors. For the ML part, we compare various approaches, including both kernel-based learning and more general neural networks. We validate the selected ML model experimentally. For the closed-loop control part, we first explore Jacobian formulations using both synthetic models and numerical approximations from experimental data. We then implement integral control actions using both these Jacobians, and evaluate them experimentally. In an experimental validation, we demonstrate that the hybrid control approach achieves setpoint regulation in a robot with 6 inputs and 4 outputs.

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Section: Introductionmentioning

confidence: 99%

Hybrid Control of Soft Robotic Manipulator

Garriga-Casanovas,

Shakib,

Ferrandy

et al. 2024

Preprint

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A variable stiffness design method for soft robotic fingers based on grasping force compensation and linearization

Shan,

Xu,

2024

Robotica

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Soft fingers play an increasingly important role in robotic grippers to achieve adaptive grasping with variable stiffness features. Previous studies of soft finger design have primarily focused on the optimization of the structural parameters of existing finger structures, but limited efforts have been put into the design methodology from fundamental grasping mechanisms to finger structures with desired grasping force features. To this aim, a fundamental architecture of soft fingers is proposed for analyzing common soft finger features and the influence of the internal structures on the overall grasping performance. In addition, three general performance metrics are introduced to evaluate the grasping performance of soft finger designs. Then, a novel method is proposed to combine the variable stiffness structure with the fundamental architecture to compensate for the grasping force of the finger and linearization. Subsequently, an embodiment design is proposed with a cantilever spring-based variable stiffness (CSVS) mechanism based on the method, and a multi-objective optimization method is employed to optimize the design. Besides, the CSVS features are analyzed through finite element analysis (FEA), and by comparing the grasping performance between the V-shape finger and the CSVS finger, it is demonstrated that the design method can effectively shorten the pre-grasp stage and linearize the grasping force in the post-grasp stage while reducing the likelihood of sliding friction between the finger and the grasped object. Finally, experiments are conducted to validate the accuracy of the FEA model, the effectiveness of the design methodology, and the adaptability of the CSVS finger.

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A novel morphing soft robot design to minimize deviations

Ferrandy,

Garriga-Casanovas,

Franco

et al. 2024

Robotica

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Soft robotics is rapidly advancing, particularly in medical device applications. A particular miniaturized manipulator design that offers high dexterity, multiple degrees-of-freedom, and better lateral force rendering than competing designs, has great potential for minimally invasive surgery. However, it faces challenges such as the tendency to suddenly and unpredictably deviate in bending plane orientation at higher pressures. In this work, we identified the cause of this deviation as the buckling of the partition wall and proposed design alternatives along with their manufacturing process to address the problem without compromising the original design features. In both simulation and experiment, the novel design managed to achieve a better bending performance in terms of stiffness and reduced deviation of the bending plane. We also developed an artificial neural network-based inverse kinematics model to further improve the performance of the prototype during vectorization. This approach yielded mean absolute errors in orientation of the bending plane below $5^{\circ }$ .

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Modeling of a two-degree-of-freedom fiber-reinforced soft pneumatic actuator

Cited by 7 publications

References 22 publications

Hybrid Control of Soft Robotic Manipulator

Hybrid Control of Soft Robotic Manipulator

A variable stiffness design method for soft robotic fingers based on grasping force compensation and linearization

A novel morphing soft robot design to minimize deviations

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