A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior

Jiang, Hao; Wang, Zhanchi; Liu, Xinghua; Chen, Xiaotong; Jin, Yusong; You, Xuanke; Chen, Xiaoping

doi:10.1109/icra.2017.7989727

Cited by 51 publications

(30 citation statements)

References 18 publications

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“…Note the similarity to the first implementation in Figure 3 combining both model-based and model-free approaches was proposed in Refs. [52][53][54] In Ref., 52 the manipulator is modeled as multiple sections with one translational and two rotational DoF. Then, multiple neural networks are used to resolve redundancy and to obtain the mapping from the task space to the high-dimension configuration space.…”

Section: Model-free Static Controllersmentioning

confidence: 99%

“…A noticeable limitation of such a method is the high sensory information required, which in the article, the authors have synthesized from certain empirical data. A polar method was adopted in Ref., 53 with the configuration space to task space mapping being analytically modeled using the PCC approximation. The actuator space to configuration space mapping is learned also considering possible first-order viscoelastic effects.…”

Section: Model-free Static Controllersmentioning

confidence: 99%

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Control Strategies for Soft Robotic Manipulators: A Survey

et al. 2018

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With the rise of soft robotics technology and applications, there have been increasing interests in the development of controllers appropriate for their particular design. Being fundamentally different from traditional rigid robots, there is still not a unified framework for the design, analysis, and control of these high-dimensional robots. This review article attempts to provide an insight into various controllers developed for continuum/soft robots as a guideline for future applications in the soft robotics field. A comprehensive assessment of various control strategies and an insight into the future areas of research in this field are presented.

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Section: Model-free Static Controllersmentioning

confidence: 99%

Section: Model-free Static Controllersmentioning

confidence: 99%

Control Strategies for Soft Robotic Manipulators: A Survey

et al. 2018

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“…Recently, different strategies to provide generic approaches for soft robot control have been proposed. In [23], authors propose a solution for the inverse kinematics problem and a model free control method is presented in [24]. This last method is based on machine learning and seems not to require any assumptions about the model, but is for now restricted to open-loop.…”

Section: B Controlmentioning

confidence: 99%

Control Design for Soft Robots based on Reduced Order Model

Thieffry

Kruszewski

Duriez

et al. 2018

IEEE Robot. Autom. Lett.

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Inspired by nature, soft robots promise disruptive advances in robotics. Soft robots are naturally compliant and exhibit nonlinear behavior, which makes their study challenging. No unified framework exists to control these robots, especially when considering their dynamics. This work proposes a methodology to study this type of robots around a stable equilibrium point. It can make the robot converge faster and with reduced oscillations to a desired equilibrium state. Using computational mechanics, a large-scale dynamic model of the robot is obtained and model reduction algorithms enable the design of low order controller and observer. A real robot is used to demonstrate the interest of the results.

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“…For other shapes, geometrically exact models or Finite Element Method have been proposed (Trivedi et al, 2008 ; Renda et al, 2012 ; Duriez, 2013 ; Gong et al, 2018 ). Machine learning techniques can also be used to develop such mappings in a model-free manner (Giorelli et al, 2013 ; George Thuruthel et al, 2017 ; Jiang et al, 2017 ). Refer to Sadati et al ( 2017 ) for a detailed comparison into multiple static modeling techniques.…”

Section: Introductionmentioning

confidence: 99%

First-Order Dynamic Modeling and Control of Soft Robots

2020

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Modeling of soft robots is typically performed at the static level or at a second-order fully dynamic level. Controllers developed upon these models have several advantages and disadvantages. Static controllers, based on the kinematic relations tend to be the easiest to develop, but by sacrificing accuracy, efficiency and the natural dynamics. Controllers developed using second-order dynamic models tend to be computationally expensive, but allow optimal control. Here we propose that the dynamic model of a soft robot can be reduced to first-order dynamical equation owing to their high damping and low inertial properties, as typically observed in nature, with minimal loss in accuracy. This paper investigates the validity of this assumption and the advantages it provides to the modeling and control of soft robots. Our results demonstrate that this model approximation is a powerful tool for developing closed-loop task-space dynamic controllers for soft robots by simplifying the planning and sensory feedback process with minimal effects on the controller accuracy.

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A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior

Cited by 51 publications

References 18 publications

Control Strategies for Soft Robotic Manipulators: A Survey

Control Strategies for Soft Robotic Manipulators: A Survey

Control Design for Soft Robots based on Reduced Order Model

First-Order Dynamic Modeling and Control of Soft Robots

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