Cosserat Rod-Based Dynamic Modeling of Tendon-Driven Continuum Robots: A Tutorial

Janabi–Sharifi, Farrokh; Jalali, Amir; Walker, Ian D.

doi:10.1109/access.2021.3077186

Cited by 59 publications

(34 citation statements)

References 57 publications

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“…The continuum model is an infinite degree of freedom model in which a robot is represented by a continuous stack of infinite infinitesimal particles (Cosserat bar theory, see [78]). In the field of robotics, the Cosserat theory consisting of a finite number of solids projected on a continuous backbone has been applied to the dynamics of hyperredundant robots [79]. Recently, the Cosserat theory has been explicitly applied to soft robot motion and operation under static and dynamic conditions [79][80][81].…”

Section: Continuum Modelmentioning

confidence: 99%

“…In the field of robotics, the Cosserat theory consisting of a finite number of solids projected on a continuous backbone has been applied to the dynamics of hyperredundant robots [79]. Recently, the Cosserat theory has been explicitly applied to soft robot motion and operation under static and dynamic conditions [79][80][81]. The Cosserat model treats the continuum robot as a deformable curve in which each particle is rigidly connected to a set of orthogonal vectors (controllers) to characterize its direction [82].…”

Section: Continuum Modelmentioning

confidence: 99%

“…In Cosserat theory [79], the configuration of a microsolid with material abscissa X ∈ ½0, L on the continuum robot with respect to the base frame of a continuum robot can be represented by position vector P and rotation matrix R. Therefore, the configuration space is defined as a curve gð⋅Þ: X ↦ gðXÞ ∈ SEð3Þ and…”

Section: Continuum Modelmentioning

confidence: 99%

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A Survey on Design, Actuation, Modeling, and Control of Continuum Robot

et al. 2022

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In this paper, we describe the advances in the design, actuation, modeling, and control field of continuum robots. After decades of pioneering research, many innovative structural design and actuation methods have arisen. Untethered magnetic robots are a good example; its external actuation characteristic allows for miniaturization, and they have gotten a lot of interest from academics. Furthermore, continuum robots with proprioceptive abilities are also studied. In modeling, modeling approaches based on continuum mechanics and geometric shaping hypothesis have made significant progress after years of research. Geometric exact continuum mechanics yields apparent computing efficiency via discrete modeling when combined with numerical analytic methods such that many effective model-based control methods have been realized. In the control, closed-loop and hybrid control methods offer great accuracy and resilience of motion control when combined with sensor feedback information. On the other hand, the advancement of machine learning has made modeling and control of continuum robots easier. The data-driven modeling technique simplifies modeling and improves anti-interference and generalization abilities. This paper discusses the current development and challenges of continuum robots in the above fields and provides prospects for the future.

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Section: Continuum Modelmentioning

confidence: 99%

Section: Continuum Modelmentioning

confidence: 99%

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A Survey on Design, Actuation, Modeling, and Control of Continuum Robot

et al. 2022

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“…Denavit-Hartenberg method is easy to apply but does not accommodate torsion very well. Cosserat rod theory 103 is promising in terms of accuracy, but its computational efficiency is concerning in applications requiring real-time closedloop control. In the literature, some critical factors such as deadzone, hysteresis, slack, 94 backlash, friction, external loading, and gravity 64,67,85 have been excluded to simplify the modelling steps.…”

Section: Modelling Of Continuum Robotsmentioning

confidence: 99%

Visual servoing of continuum robots: Methods, challenges, and prospects

Nazari

Zareinia

Janabi–Sharifi

2022

Robotics Computer Surgery

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Background Recent advancements in continuum robotics have accentuated developing efficient and stable controllers to handle shape deformation and compliance. The control of continuum robots (CRs) using physical sensors attached to the robot, particularly in confined spaces, is difficult due to their limited accuracy in three‐dimensional deflections and challenging localisation. Therefore, using non‐contact imaging sensors finds noticeable importance, particularly in medical scenarios. Accordingly, given the need for direct control of the robot tip and notable uncertainties in the kinematics and dynamics of CRs, many papers have focussed on the visual servoing (VS) of CRs in recent years. Methods The significance of this research towards safe human‐robot interaction has fuelled our survey on the previous methods, current challenges, and future opportunities. Results Beginning with actuation modalities and modelling approaches, the paper investigates VS methods in medical and non‐medical scenarios. Conclusions Finally, challenges and prospects of VS for CRs are discussed, followed by concluding remarks.

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“…For modelling our continuum robot, we have adapted the tendon-driven dynamic Cosserat rod model in [5][25] [26]. More discussion about using tendon-length control are presented in [5] [23].…”

Section: Mathematical Modelmentioning

confidence: 99%

A Data-Efficient Model-Based Learning Framework for the Closed-Loop Control of Continuum Robots

Wang,

Rojas

2022

Preprint

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Traditional dynamic models of continuum robots are in general computationally expensive and not suitable for real-time control. Recent approaches using learning-based methods to approximate the dynamic model of continuum robots for control have been promising, although real data hungry-which may cause potential damage to robots and be time consuming-and getting poorer performance when trained with simulation data only. This paper presents a modelbased learning framework for continuum robot closed-loop control that, by combining simulation and real data, shows to require only 100 real data to outperform a real-data-only controller trained using up to 10000 points. The introduced data-efficient framework with three control policies has utilized a Gaussian process regression (GPR) and a recurrent neural network (RNN). Control policy A uses a GPR model and a RNN trained in simulation to optimize control outputs for simulated targets; control policy B retrains the RNN in policy A with data generated from the GPR model to adapt to real robot physics; control policy C utilizes policy A and B to form a hybrid policy. Using a continuum robot with soft spines, we show that our approach provides an efficient framework to bridge the sim-toreal gap in model-based learning for continuum robots.

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Cosserat Rod-Based Dynamic Modeling of Tendon-Driven Continuum Robots: A Tutorial

Cited by 59 publications

References 57 publications

A Survey on Design, Actuation, Modeling, and Control of Continuum Robot

A Survey on Design, Actuation, Modeling, and Control of Continuum Robot

Visual servoing of continuum robots: Methods, challenges, and prospects

A Data-Efficient Model-Based Learning Framework for the Closed-Loop Control of Continuum Robots

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