SoGut: A Soft Robotic Gastric Simulator

Dang, Yu; Liu, Yuanxiang; Hashem, Ryman; Bhattacharya, Dipankar; Allen, Jacqueline; Stommel, Martin; Cheng, Leo K.; Xu, Weiliang

doi:10.1089/soro.2019.0136

Cited by 41 publications

(21 citation statements)

References 32 publications

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“…Silicone elastomers and other polymers used in soft robotics literature, as well as the corresponding number of citations. For this graph, 45 articles were examined: Ecoflex [198][199][200][201][202][203][204][205], Dragon Skin 10/20/30/FX-Pro [206][207][208][209][210][211][212][213][214][215][216][217][218][219][220][221][222][223][224][225], Elastosil M4601 [199,202,218,[226][227][228][229], PDMS [198,208,[230][231][232][232][233][234], Smooth-Sil [200,207,209,218,222,…”

Section: Figurementioning

confidence: 99%

Soft Grippers for Automatic Crop Harvesting: A Review

Navas

Fernández

Sepúlveda

et al. 2021

Sensors

140

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Agriculture 4.0 is transforming farming livelihoods thanks to the development and adoption of technologies such as artificial intelligence, the Internet of Things and robotics, traditionally used in other productive sectors. Soft robotics and soft grippers in particular are promising approaches to lead to new solutions in this field due to the need to meet hygiene and manipulation requirements in unstructured environments and in operation with delicate products. This review aims to provide an in-depth look at soft end-effectors for agricultural applications, with a special emphasis on robotic harvesting. To that end, the current state of automatic picking tasks for several crops is analysed, identifying which of them lack automatic solutions, and which methods are commonly used based on the botanical characteristics of the fruits. The latest advances in the design and implementation of soft grippers are also presented and discussed, studying the properties of their materials, their manufacturing processes, the gripping technologies and the proposed control methods. Finally, the challenges that have to be overcome to boost its definitive implementation in the real world are highlighted. Therefore, this review intends to serve as a guide for those researchers working in the field of soft robotics for Agriculture 4.0, and more specifically, in the design of soft grippers for fruit harvesting robots.

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Section: Figurementioning

confidence: 99%

Soft Grippers for Automatic Crop Harvesting: A Review

Navas

Fernández

Sepúlveda

et al. 2021

Sensors

140

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“…Advanced control techniques like the MPC relies on developing a suitable plant model for optimum performance. Soft robots like the RoSE [12] and soft robotic gastric simulator [13] manufactured by soft elastomers like silicone rubber, cannot be modeled from the perspective of first principles due to their complex, continuous, and highly compliant intrinsic deformation, which leads to infinite degrees of freedom [14]. Developments in the area of Machine Learning (ML) and data science have opened the gateway to model RoSE with data-driven techniques [15].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Predictive Control of a Robotic Soft Esophagus

Bhattacharya¹,

Hashem²,

Cheng³

et al. 2022

IEEE Trans. Ind. Electron.

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Strictures caused by esophageal cancer can narrow down the esophageal lumen, leading to dysphagia. Palliation of dysphagia has driven the development of a Robotic Soft Esophagus (RoSE) to provide a novel in vitro platform for esophageal stent testing and food viscosity studies. In RoSE, peristaltic wave generation and control were done in an open-loop manner since the conduit lacked visibility and embedded sensing capability. Hence, in this work, RoSE version 2.0 (RoSEv2.0) was designed with embedded Time Of Flight (TOF) and pressure sensors to measure conduit displacement and air pressure, respectively, for modeling and control. Model Predictive Control (MPC) of RoSEv2.0 was implemented to govern the peristalsis and air pressure profile autonomously. The implemented MPC used Sparse Identification Nonlinear Dynamics with Control (SINDYC) models to estimate the future states of ROSEv2.0. The dynamic models were discovered from the TOF and pressure sensors captured data. Peristalsis waves of speed 20 mm/s, wavelength 75 mm, and amplitudes 5, 7.5, and 10 mm were successfully generated by the MPC. Additionally, RoSEv2.0 with the MPC was employed to perform stent migration testing with various food boluses consistencies.

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“…Nature provides excellent inspiration for many soft robots with unique functions, ranging from spiders [ 1 ] and fish [ 2 ] to worms. [ 3 ] In the biomedical field, soft devices comprising multiple actuating elements have been developed for prosthetics [ 4,5 ] or assist devices (including gloves, [ 6,7 ] exoskeletons, [ 8,9 ] cardiac assist devices [ 10,11 ] ), body‐part simulators [ 12–15 ] and artificial organs. [ 16,17 ]…”

Section: Introductionmentioning

confidence: 99%

Computational Design of a Soft Robotic Myocardium for Biomimetic Motion and Function

Park

Ozturk

Roche

2022

Adv Funct Materials

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Soft robotic devices containing multiple actuating elements have successfully recapitulated complex biological motion, leading to their utility in biomedical applications. However, there are inherent nonlinear mechanics associated with soft composite materials where soft actuators are embedded in elastomeric matrices. Predicting their overall behavior prior to fabrication and subsequent experimental characterization can therefore present a hurdle in the design process and in efficiently satisfying functional requirements and specifications. In this work, a computational design framework for optimizing the motion and function of biomimetic soft robotic composites is demonstrated by conducting a design case study of soft robotic cardiac muscle (myocardium) with a particular focus on applications including replicating and assisting cardiac motion and function. A finite element model of a soft robotic myocardium is built, in which actuators are prescribed with anisotropic strain to simulate local deformation, and various design parameters are investigated by evaluating the performance of each configuration in terms of ventricular twist, volumetric output, and pressure generation. Then, an optimized design is proposed that recapitulates the physiological motion and hemodynamics of the heart, and its thrombogenicity is further explored using a fluid-structure interaction model. This framework has broader utility in predicting the performance of other soft robotic embedded composites.

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SoGut: A Soft Robotic Gastric Simulator

Cited by 41 publications

References 32 publications

Soft Grippers for Automatic Crop Harvesting: A Review

Soft Grippers for Automatic Crop Harvesting: A Review

Nonlinear Model Predictive Control of a Robotic Soft Esophagus

Computational Design of a Soft Robotic Myocardium for Biomimetic Motion and Function

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