Closed-Form Equations and Experimental Verification for Soft Robot Arm Based on Cosserat Theory

Niu, Lizhou; Ding, Lixin; Gao, Haibo; Su, Youfeng; Deng, Zongquan; Liu, Zhen

doi:10.1109/iros40897.2019.8968477

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…For a comprehensive study on the modeling of continuum robots, see [25,26]. [23] Cosserat Linear × × 7% Wang et al [27] Cosserat Linear × -Dou et al [28] Euler-Bernoulli Linear × Less than 8% Huang et al [29] Variable Curvature Linear × × 2.89% Niu et al [30] Cosserat Linear × × Less than 4% Ghoreishi et al [31] Euler-Bernoulli Linear × -Li et al [32] Cosserat Linear × Less than 5% Caasenbrood et al [33] Piece-wise Constant Curvature Non-Linear × RMS error was ±0.55…”

Section: Related Studiesmentioning

confidence: 99%

Hyperelastic Modeling and Validation of Hybrid-Actuated Soft Robot with Pressure-Stiffening

et al. 2023

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Soft robots have gained popularity, especially in intraluminal applications, because their soft bodies make them safer for surgical interventions than flexures with rigid backbones. This study investigates a pressure-regulating stiffness tendon-driven soft robot and provides a continuum mechanics model for it towards using that in adaptive stiffness applications. To this end, first, a central single-chamber pneumatic and tri-tendon-driven soft robot was designed and fabricated. Afterward, the classic Cosserat’s rod model was adopted and augmented with the hyperelastic material model. The model was then formulated as a boundary-value problem and was solved using the shooting method. To identify the pressure-stiffening effect, a parameter-identification problem was formulated to identify the relationship between the flexural rigidity of the soft robot and internal pressure. The flexural rigidity of the robot at various pressures was optimized to match theoretical deformation and experiments. The theoretical findings of arbitrary pressures were then compared with the experiment for validation. The internal chamber pressure was in the range of 0 to 40 kPa and the tendon tensions were in the range of 0 to 3 N. The theoretical and experimental findings were in fair agreement for tip displacement with a maximum error of 6.40% of the flexure’s length.

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Section: Related Studiesmentioning

confidence: 99%

Hyperelastic Modeling and Validation of Hybrid-Actuated Soft Robot with Pressure-Stiffening

et al. 2023

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Dynamic Task Space Control Enables Soft Manipulators to Perform Real‐World Tasks

Fischer

Toshimitsu

Kazemipour

et al. 2022

Advanced Intelligent Systems

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An emerging alternative to conventional rigid robotics is the field of soft robotics. [1][2][3][4] Soft robots adopt the principle of physical artificial intelligence to achieve an inherently compliant and embodied robotic behavior. [5,6] In soft robotics, robots are fabricated mainly from functional soft materials [7][8][9][10] to achieve inherently adaptive and intelligent characteristics. The inherent compliance of soft robots enables various behaviors that were previously difficult for conventional, rigid-linked robots to achieve, such as navigating through tight environments or gentle gripping. [11][12][13] Continuum robots [14][15][16] have shown improved adaptability and safety when moving from rigid to soft actuator materials and support structures. However, it is challenging to model continuum robots' behavior due to the infinite dimensionality of their state space.There have been several approaches to construct a dynamic model for soft continuum manipulators, akin to the manipulator equation. [17,18] Godage et al. [19] derived the dynamic model of a continuum manipulator through integral Lagrangian formulation with the assumption of continuous mass distribution along the arm. However, the closed-form expressions of dynamic terms become complicated as the number of segments increases, making the model unpractical for using it in realtime control. In order to simplify the dynamic model, the mass distribution of each segment was approximated into a single lumped mass. Under this assumption, Falkenhahn et al. derived the dynamic model for a continuously bending manipulator (Festo Bionic Handling Assistant) where the lumped masses are assumed to be concentrated in the tips of the soft continuum sections. [20] With the addition of a dynamic model, they showed acceleration-level control methods in joint space. In a later work, Falkenhahn et al. explicitly considered valve dynamics to achieve higher model accuracy. [21] Curvature space methods can also be combined with inverse kinematic approaches to control real-world coordinates. [22,23] This combination was shown by Gong et al., [24] who used an underwater continuum arm to grab samples. Alternatively, one can use machine learning methods to obtain a model of the dynamics. Thuruthel et al. use a recurrent neural network to approximate the dynamics, and learn a controller. [25] However, the blackbox nature of neural networks is undesirable for control.Alternatively, it is possible to compute the dynamic parameters of a continuously bending soft body by using an augmented rigid body model to approximate its kinematic and dynamic characteristics. [26,27] This model supplements the piecewise constant curvature (PCC) model by adding a rigid link model and mass

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A Hybrid Control Approach for a Pneumatic-Actuated Soft Robot

Tavio y Cabrera,

Santina,

Borja

2024

Springer Proceedings in Advanced Robotics

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Closed-Form Equations and Experimental Verification for Soft Robot Arm Based on Cosserat Theory

Cited by 5 publications

References 23 publications

Hyperelastic Modeling and Validation of Hybrid-Actuated Soft Robot with Pressure-Stiffening

Hyperelastic Modeling and Validation of Hybrid-Actuated Soft Robot with Pressure-Stiffening

Dynamic Task Space Control Enables Soft Manipulators to Perform Real‐World Tasks

A Hybrid Control Approach for a Pneumatic-Actuated Soft Robot

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