Soft Robots Modeling: A Structured Overview

Armanini, Costanza; Boyer, Frédéric; Mathew, Anup Teejo; Duriez, Christian; Renda, Federico

doi:10.1109/tro.2022.3231360

Cited by 128 publications

(64 citation statements)

References 158 publications

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“…For detailed review of learning-based methods, the reader can refer to recent articles on modeling and control. [26][27][28][29][30][31] 4. Sensing, Control, and Planning…”

Section: Learning-based (Model-less) Methodsmentioning

confidence: 99%

“…Recent surveys have proposed compelling categorizations, mostly based on system kinematics, to classify relevant modeling techniques. [26,29] In this survey, we adapt a systematic approach based on the standard convention in classic continuum mechanics research, thus classifying methods according to the following: 1) Kinematics, which define the robot configuration with either absolute [96][97][98] or relative [99] coordinate frames. Absolute models refer to a fixed inertial frame, whereas relative models define the configuration in relative frames that depend on the robot's motion.…”

Section: Modelingmentioning

confidence: 99%

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Continuum Robots: An Overview

Russo

Sadati

Dong

et al. 2023

Advanced Intelligent Systems

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When thinking of robots, the image that immediately springs to mind is either an android, designed to resemble human beings and mimic their behavior, or an industrial manipulator, for example, a large machine of rigid steel working in an assembly line. However, robotic systems of multiple forms have been developed to carry out the most diverse tasks, with designs that evolved from readily available structures, such as vehicles, or inspired by nature. In the last decade, more and more bioinspired designs have been enabled by advances in computer science, materials, and manufacturing. Among them, continuum robots, inspired by snakes, trunks, and tendrils, are characterized by a compliant backbone capable of continuous bending, whose shape (configuration, i.e., position and orientation along the backbone curve) is controlled by applying loads through onboard "intrinsic" actuators (e.g., pneumatics or hydraulics) or transmission elements (e.g., tendons, rods, or compliant tubes) that are pushed/pulled from an extremity of the backbone ("extrinsic actuation"). These robots have flexible bodies with a high length to cross-section diameter ratio and are uniquely suited to tasks that require the deployment of tools or sensors with a long reach into tortuous and narrow paths. [1] Continuum robots were first developed in the 1960s [2] and rose to prominence in the late 1990s, [3] when they were often called elephant trunk, [4] tentacle/tendril, [5] or flexible [6] manipulators. Whereas other robots were characterized by intrinsic actuation and larger sizes, research on continuum robots first focused on miniaturization for medical applications, [7,8] leading to extrinsic actuation to enable leaner designs. Recently, continuum robots have been developed for a wider range of applications, including manufacturing, aerospace, search and rescue, and nuclear. In such scenarios, the infinite degrees of freedom (DoFs) of these slender robots enable inspection and intervention in areas that cannot be accessed by conventional robots (e.g., tunnels [9] and gas turbines [10] ).An exhaustive literature search (see Appendix) outlines a surging interest in continuum robots, with a 19.6% average annual growth in publications between 2012 and 2021 (continuum robot search on Web of Science). Three main topics can be observed in recent literature surveys, listed in Table 1. DesignTendon-driven and concentric tube robots are predominant in surveys, but they are not the only design solutions. [18] This richer literature can be attributed to intense research effort toward medical and other small-scale applications when compared to larger-scale tasks (e.g., manipulation) where intrinsic actuation is advantageous.

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“…For detailed review of learning-based methods, the reader can refer to recent articles on modeling and control. [26][27][28][29][30][31] 4. Sensing, Control, and Planning…”

Section: Learning-based (Model-less) Methodsmentioning

confidence: 99%

Section: Modelingmentioning

confidence: 99%

Continuum Robots: An Overview

Russo

Sadati

Dong

et al. 2023

Advanced Intelligent Systems

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“…The practical computation of this value depends on the mechanical model used to describe the body of the continuum robot. A geometrically exact solution can be obtained by defining the robot body as a beam through Euler–Bernoulli, Kirchhoff, or Cosserat beam theories [ 39 , 40 ]. For each segment with length

of the robot backbone, independently from the robot design, a local stiffness coefficient can be then evaluated through material properties (such as Young’s modulus

) and geometry (such as second moment of area

) when material mechanics are linear or can be linearized.…”

Section: Methodsmentioning

confidence: 99%

Continuum Robots: From Conventional to Customized Performance Indicators

et al. 2023

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Continuum robots have often been compared with rigid-link designs through conventional performance metrics (e.g., precision and Jacobian-based indicators). However, these metrics were developed to suit rigid-link robots and are tuned to capture specific facets of performance, in which continuum robots do not excel. Furthermore, conventional metrics either fail to capture the key advantages of continuum designs, such as their capability to operate in complex environments thanks to their slender shape and flexibility, or see them as detrimental (e.g., compliance). Previous work has rarely addressed this issue, and never in a systematic way. Therefore, this paper discusses the facets of a continuum robot performance that cannot be characterized by existing indicator and aims at defining a tailored framework of geometrical specifications and kinetostatic indicators. The proposed framework combines the geometric requirements dictated by the target environment and a methodology to obtain bioinspired reference metrics from a biological equivalent of the continuum robot (e.g., a snake, a tentacle, or a trunk). A numerical example is then reported for a swimming snake robot use case.

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“…As a result, traditional modeling and control methods that assume rigid links are not directly applicable [3]. These challenges led to the development of alternative modeling [4] and control strategies [5].…”

Section: Introductionmentioning

confidence: 99%

Ablation Study of a Dynamic Model for a 3D-Printed Pneumatic Soft Robotic Arm

2023

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Ongoing advancements in the design and fabrication of soft robots are creating new challenges in modeling and control. This paper presents a dynamic Cosserat rod model for a single-section 3D-printed pneumatic soft robotic arm capable of combined stretching and bending. The model captures the manufacturing variability of the actuators by tuning the pressure-strain relation for each actuator. Moreover, it includes a simple model of the pneumatic actuation system that incorporates the transient response of proportional pressure-controlled electronic valves. The model was validated experimentally for several quasi-static and dynamic motion patterns with actuation frequencies ranging from 0.2 Hz to 20 Hz. The model reproduced the quasi-static experiments with an average tip error of 4.83% of the arm length. In dynamic conditions, the average tip error was 4.33% for stretching and bending motions, 5.64% for five motor babbling experiments, and 22.53% for three challenging sinusoidal patterns. An ablation study of the model components found that the most influential factors for the average accuracy were gravity and strain gains, followed by damping and pressure transient. This work could assist researchers in focusing on the most significant aspects for closing the real-to-sim gap when modeling pneumatic soft robotic arms.

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Soft Robots Modeling: A Structured Overview

Cited by 128 publications

References 158 publications

Continuum Robots: An Overview

Continuum Robots: An Overview

Continuum Robots: From Conventional to Customized Performance Indicators

Ablation Study of a Dynamic Model for a 3D-Printed Pneumatic Soft Robotic Arm

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