Underwater Dynamic Modeling for a Cable-Driven Soft Robot Arm

Xu, Fan; Wang, Hesheng; Au, Kwok Wai Samuel; Chen, Weidong; Miao, Yanzi

doi:10.1109/tmech.2018.2872972

Cited by 42 publications

(25 citation statements)

References 49 publications

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“…Content may change prior to final publication. [46]. However, a few could develop untethered soft robot that successfully mimics fish movement at a reasonable speed as the work proposed in [47].…”

Section: B Biomimicry Applications Of Biohybrid Soft Robotsmentioning

confidence: 99%

Biohybrid Soft Robots, E-Skin, and Bioimpedance Potential to Build Up Their Applications: A Review

et al. 2020

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Section: B Biomimicry Applications Of Biohybrid Soft Robotsmentioning

confidence: 99%

Biohybrid Soft Robots, E-Skin, and Bioimpedance Potential to Build Up Their Applications: A Review

et al. 2020

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“…However, developing agile, dexterous, and reliable underwater soft robots faces substantial challenges in structural design, actuation, modeling, and control. Soft manipulators have been applied for performing underwater grasping tasks in very recent studies owing to their outstanding environment adaptability and safety (Teeples et al 2018;Liu et al 2020;Kurumaya et al 2018;Mura et al 2018;Xu et al 2018). Soft manipulators generally have strong nonlinearities (e.g., asymmetric hysteresis, creep, and so on) due to the characteristics of the materials used as actuators and structures (Hosovsky et al 2016;Shiva et al 2016;Stilli et al 2017;Pawlowski et al 2019;Thérien and Plante 2016).…”

Section: Introductionmentioning

confidence: 99%

Prediction model-based learning adaptive control for underwater grasping of a soft manipulator

Yang

Liu

Fang

et al. 2021

Int J Intell Robot Appl

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Soft robotic manipulators have promising features for performing non-destructive underwater tasks. Nevertheless, soft robotic systems are sensitive to the inherent nonlinearity of soft materials, the underwater flow current disturbance, payload, etc. In this paper, we propose a prediction model-based guided reinforcement learning adaptive controller (GRLMAC) for a soft manipulator to perform spatial underwater grasping tasks. In the GRLMAC, a feed-forward prediction model (FPM) is established for describing the length/pressure hysteresis of a chamber in the soft manipulator. Then, the online adjustment for FPM is achieved by reinforcement learning. Introducing the human experience into the reinforcement learning method, we can choose an appropriate adjustment action for the FPM from the action space without the offline training phase, allowing online adjusting the inflation pressure. To demonstrate the effectiveness of the controller, we tested the soft manipulator in the pumped flow current and different gripping loads. The results show that GRLMAC acquires promising accuracy, robustness, and adaptivity. We envision that the soft manipulator with online learning would endow future underwater robotic manipulation under natural turbulent conditions.

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“…In recent years, as one of the typical representatives, soft robots have made great technological breakthroughs in scientific and technological innovation (Trivedi et al, 2008;Albu-Schaffer et al, 2008;Kim et al, 2013;Rus and Tolley, 2015). Soft robots are a class of typical continuum robots that made of soft materials, and their structural ontology can be continuously deformed in limited space, mimicking the continuous movement away organisms and changing their shape to adapt to the characteristics of the environment (Xu et al, 2018;Laschi et al, 2012). Compared with the traditional rigid manipulators, soft manipulators have the advantages of multidegree-of-freedoms (DOFs) and good environmental adaptability (Calisti et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Static modeling and analysis of soft manipulator considering environment contact based on segmented constant curvature method

Chen

Gong

2020

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Purpose The purpose of this paper is to estimate the deformation of soft manipulators caused by obstacles accurately and the contact force and workspace can be also predicted. Design/methodology/approach The continuum deformation of the backbone of the soft manipulator under contact is regarded as two constant curvature arcs and the curvatures are different according to the fluid pressure and obstacle location based on piecewise constant curvature framework. Then, this study introduces introduce the moment balance and energy conservation equation to describe the static relationship between driving moment, elastic moment and contact moment. Finally, simulation and experiments are carried out to verify the accuracy of the proposed model. Findings For rigid manipulators, environmental contact except for the manipulated object was usually considered as a “collision” which should be avoided. For soft manipulators, an environment is an important tool for achieving manipulation goals and it might even be considered to be a part of the soft manipulator’s end-effector in some specified situations. Research limitations/implications There are also some limitations to the presented study. Although this paper has made progress in the static modeling under environmental contact, some practical factors still limit the further application of the model, such as the detection accuracy of the environment location and the deformation of the contact surface. Originality/value Based on the proposed kinematic model, the bending deformation with environmental contact is discussed in simulations and has been experimentally verified. The comparison results show the correctness and accuracy of the presented SCC model, which can be applied to predict the slender deformation under environmental contact without knowing the contact force.

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Underwater Dynamic Modeling for a Cable-Driven Soft Robot Arm

Cited by 42 publications

References 49 publications

Biohybrid Soft Robots, E-Skin, and Bioimpedance Potential to Build Up Their Applications: A Review

Biohybrid Soft Robots, E-Skin, and Bioimpedance Potential to Build Up Their Applications: A Review

Prediction model-based learning adaptive control for underwater grasping of a soft manipulator

Static modeling and analysis of soft manipulator considering environment contact based on segmented constant curvature method

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