FEM-based training of artificial neural networks for modular soft robots

Runge, Gundula; Wiese, Mats; Raatz, Annika

doi:10.1109/robio.2017.8324448

Cited by 39 publications

(20 citation statements)

References 29 publications

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“…Giorelli et al [30], [31] used a Feed-forward Neural Network (FNN) to learn the tip position of a cable-driven soft tentacle based on the cable forces. Runge et al [32] suggested Finite Element Analysis (FEA) based training to learn a kinematic model of a soft pneumatic actuator through a neural network. Neural networks have also been applied to calibrate soft sensors to estimate the magnitude and the location of a contact pressure [33].…”

Section: Introductionmentioning

confidence: 99%

Color-Based Proprioception of Soft Actuators Interacting With Objects

Scharff

Doornbusch

Doubrovski

et al. 2019

IEEE/ASME Trans. Mechatron.

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Actuators using soft materials feature a large number of degrees of freedom. This tremendous flexibility allows a soft actuator to passively adapt its shape to the objects under interaction. In this paper we propose a novel proprioception method for soft actuators during real-time interaction with priorly unknown objects. Firstly, we design a color-based sensing structure that instantly translates the inflation of a bellow into changes in color, which are subsequently detected by a miniaturized color sensor. The color sensor is small and thus multiple of them can be integrated into soft pneumatic actuators to reflect local deformations. Secondly, we make use of a Feedforward Neural Network (FNN) to reconstruct a multivariate global shape deformation from local color signals. Our results demonstrate that deformations of the actuator during interaction, including sigmoid-like shapes, can be accurately reconstructed. The accurate shape sensing represents a significant step towards closed-loop control of soft robots in unstructured environments.

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Section: Introductionmentioning

confidence: 99%

Color-Based Proprioception of Soft Actuators Interacting With Objects

Scharff

Doornbusch

Doubrovski

et al. 2019

IEEE/ASME Trans. Mechatron.

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show abstract

“…This could be investigated further with advanced multimodal sensors (Chen et al 2020) and electronic skin (e-skin) (Shih et al 2020;Fu et al 2020). The FEA could be used in kinematic model development of SPAs where an adequately large amount of data is required to train the ANN (Runge, Wiese, and Raatz 2017).…”

Section: Discussion Challenges and Future Directionsmentioning

confidence: 99%

3D/4D-printed bending-type soft pneumatic actuators: fabrication, modelling, and control

Zolfagharian

Mahmud

Gharaie

et al. 2020

Virtual and Physical Prototyping

113

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This article reviews soft pneumatic actuators (SPAs) that are manufactured entirely via additive manufacturing methods. These actuators are known as four-dimensional (4D)-printed SPAs and can generate bending motions in response to either pressurized or vacuum (negative pressure) air stimulus after fabrication. They are characterized by geometrical and material factors that determine their motion trajectory, and the force they exert on manipulated soft objects in delicate applications such as food handling and non-invasive surgery. Here, we review various 3D printers and materials used for the fabrication of the pressurized air bendingtype SPAs. The reported approaches for modeling and control of these actuators are presented and compared. General discussions, as well as future directions and challenges of these actuators, are given.

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“…It can build a more accurate model of SRNCR and optimize the control strategy. For example, both [116,117] use machine learning methods to control and model SRNCRs. Therefore, it is foreseeable that in the future machine learning will become an important method for modeling and controlling SRNCRs.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Soft Rehabilitation and Nursing-Care Robots: A Review and Future Outlook

Peng

Huang

2019

Applied Sciences

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Rehabilitation and nursing-care robots have become one of the prevalent methods for assistant treatment of motor disorder patients in the field of medical rehabilitation. Traditional rehabilitation robots are mostly made of rigid materials, which significantly limits their application for medical rehabilitation and nursing-care. Soft robots show great potential in the field of rehabilitation robots because of their inherent compliance and safety when they interact with humans. In this paper, we conduct a systematic summary and discussion on the soft rehabilitation and nursing-care robots. This study reviews typical mechanical structures, modeling methods, and control strategies of soft rehabilitation and nursing-care robots in recent years. We classify soft rehabilitation and nursing-care robots into two categories according to their actuation technology, one is based on tendon-driven actuation and the other is based on soft intelligent material actuation. Finally, we analyze and discuss the future directions and work about soft rehabilitation and nursing-care robots, which can provide useful guidance and help on the development of advanced soft rehabilitation and nursing-care robots.

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FEM-based training of artificial neural networks for modular soft robots

Cited by 39 publications

References 29 publications

Color-Based Proprioception of Soft Actuators Interacting With Objects

Color-Based Proprioception of Soft Actuators Interacting With Objects

3D/4D-printed bending-type soft pneumatic actuators: fabrication, modelling, and control

Soft Rehabilitation and Nursing-Care Robots: A Review and Future Outlook

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