Dynamic control of soft robots interacting with the environment

Santina, Cosimo Della; Katzschmann, Robert K.; Biechi, Antonio; Rus, Daniela

doi:10.1109/robosoft.2018.8404895

Cited by 190 publications

(167 citation statements)

References 32 publications

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“…The overall dynamical model of the system is obtained by employing the Lagrangian analysis, similar to the approach used by Della Santina et al 32 The equation of motion can be obtained by applying the Euler-Lagrange equation formulated in Equation (3). 33 F…”

Section: Dynamic Modelingmentioning

confidence: 99%

Dynamic Modeling of Fiber-Reinforced Soft Manipulator: A Visco-Hyperelastic Material-Based Continuum Mechanics Approach

et al. 2019

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Robot-assisted surgery is gaining popularity worldwide and there is increasing scientific interest to explore the potential of soft continuum robots for minimally invasive surgery. However, the remote control of soft robots is much more challenging compared with their rigid counterparts. Accurate modeling of manipulator dynamics is vital to remotely control the diverse movement configurations and is particularly important for safe interaction with the operating environment. However, current dynamic models applied to soft manipulator systems are simplistic and empirical, which restricts the full potential of the new soft robots technology. Therefore, this article provides a new insight into the development of a nonlinear dynamic model for a soft continuum manipulator based on a material model. The continuum manipulator used in this study is treated as a composite material and a modified nonlinear Kelvin-Voigt material model is utilized to embody the visco-hyperelastic dynamics of soft silicone. The Lagrangian approach is applied to derive the equation of motion of the manipulator. Simulation and experimental results prove that this material modeling approach sufficiently captures the nonlinear time-and rate-dependent behavior of a soft manipulator. Material model-based closed-loop trajectory control was implemented to further validate the feasibility of the derived model and increase the performance of the overall system.

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Section: Dynamic Modelingmentioning

confidence: 99%

Dynamic Modeling of Fiber-Reinforced Soft Manipulator: A Visco-Hyperelastic Material-Based Continuum Mechanics Approach

et al. 2019

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“…Theorem 1. For the studied soft robot described by (12), if the neural network approximates with an accuracy index satisfying the following inequality < (14) where and are the characteristic parameters of soft robot defined in (4) and (7), then the proposed controller (13) can exponentially drive any ∈  to the desired constant position ∈ , i.e., lim →∞ ‖ ( ) − ‖ 2 = 0. Proof 1.…”

Section: Robust Controller Designmentioning

confidence: 99%

“…with , and being defined in (4), (7) and (16), such that the proposed robust controller (17) can drive to track the desired trajectory in a finite time, i.e.,…”

Section: Theoremmentioning

confidence: 99%

“…In [6], the kinematic model is deduced by using geometric information, and then a computed torque controller is applied to control an eel-like soft robot. A robust feedback control was proposed in [7] to control the trajectory of soft robots, under as well the assumption of piece-wise constant curvature. Other techniques have also been used to get the model.…”

Section: Introductionmentioning

confidence: 99%

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Robust control of a silicone soft robot using neural networks

Zheng

Zhou

2020

ISA Transactions

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A B S T R A C TThis paper deals with the robust controller design problem to regulate the position of a soft robot with elastic behavior, driven by 4 cable actuators. In this work, we first used an artificial neural network to approximate the relation between these actuators and the controlled position of the soft robot, based on which two types of robust controllers (type of integral and sliding mode) are proposed. The effectiveness and the robustness of the proposed controllers have been analyzed both for the constant and the time-varying disturbances. The performances (precision, convergence speed and robustness) of the proposed method have been validated via different experimental tests.

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“…Hybrid force and position estimation and control methods have been introduced to address these issues, relying on combining force and position sensor readings to achieve the estimation and control tasks [6], [7]. Bajo et al recently proposed a hybrid position/force control paradigm for a miniature tendon driven continuum manipulator and showcased it for stiffness imaging of soft tissue.…”

mentioning

confidence: 99%

Stiffness Imaging With a Continuum Appendage: Real-Time Shape and Tip Force Estimation From Base Load Readings

Sadati

Shiva

Herzig

et al. 2020

IEEE Robot. Autom. Lett.

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In this paper, we propose benefiting from load readings at the base of a continuum appendage for real-time forward integration of Cosserat rod model with application in configuration and tip load estimation. The application of this method is successfully tested for stiffness imaging of a soft tissue, using a 3-DOF hydraulically actuated braided continuum appendage. Multiple probing runs with different actuation pressures are used for mapping the tissue surface shape and directional linear stiffness, as well as detecting non-homogeneous regions, e.g. a hard nodule embedded in a soft silicon tissue phantom. Readings from a 6-axis force sensor at the tip is used for comparison and verification. As a result, the tip force is estimated with 0.016-0.037 N (7-20%) mean error in the probing and 0.02-0.1 N (6-12%) in the indentation direction, 0.17 mm (14%) mean error is achieved in estimating the surface profile, and 3.4-15 [N/m] (10-16%) mean error is observed in evaluating tissue directional stiffness, depending on the appendage actuation. We observed that if the appendage bends against the slider motion (toward the probing direction), it provides better horizontal stiffness estimation and better estimation in the perpendicular direction is achieved when it bends toward the slider motion (against the probing direction). In comparison with a rigid probe, ≈ 10 times smaller stiffness and ≈ 7 times larger mean standard deviation values were observed, suggesting the importance of a probe stiffness in estimation the tissue stiffness.

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Dynamic control of soft robots interacting with the environment

Cited by 190 publications

References 32 publications

Dynamic Modeling of Fiber-Reinforced Soft Manipulator: A Visco-Hyperelastic Material-Based Continuum Mechanics Approach

Dynamic Modeling of Fiber-Reinforced Soft Manipulator: A Visco-Hyperelastic Material-Based Continuum Mechanics Approach

Robust control of a silicone soft robot using neural networks

Stiffness Imaging With a Continuum Appendage: Real-Time Shape and Tip Force Estimation From Base Load Readings

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