SoPrA: Fabrication &amp; Dynamical Modeling of a Scalable Soft Continuum Robotic Arm with Integrated Proprioceptive Sensing

Toshimitsu, Yasunori; Wong, Ki Wan; Büchner, Thomas; Katzschmann, Robert K.

doi:10.1109/iros51168.2021.9636539

Cited by 35 publications

(17 citation statements)

References 25 publications

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“…In [1] the kinematic and dynamic models for SoPrA have been formulated, via PCC and Augmented Rigid Body [2]. The dynamic equation for the soft arm is defined as:…”

Section: A Mathematical Modelmentioning

confidence: 99%

“…2: Augmented Rigid Body Model: on the left the equivalent rigid joints and links, on the right the curvature-based space description. Adapted from [1].…”

Section: A Mathematical Modelmentioning

confidence: 99%

“…According to [1] the robotic arm is modeled with 2 curvature parameters per section, not the bending angle θ and off-plane rotation φ but rather two derived components:…”

Section: A Mathematical Modelmentioning

confidence: 99%

“…In this work we have applied a powerful model-based control method, the Model Predictive Control (MPC), to SoPrA Soft continuum Proprioceptive Arm [1]. This hardware is significant since it's a soft-arm able to perform dynamic motions and that can be easily scaled up and down.…”

Section: Introductionmentioning

confidence: 99%

“…We have implemented a robust MPC able to deal with model uncertainties and used it for trajectory following tasks on SoPrA [1]. We have optimized over internal and external constraints, adopting corrections to use standard MPC algorithms with the Augmented Rigid Body formulation.…”

Section: Introductionmentioning

confidence: 99%

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A Unified and Modular Model Predictive Control Framework for Soft Continuum Manipulators under Internal and External Constraints

Spinelli¹,

Katzschmann²

2022

Preprint

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The emerging field of fluidically actuated soft robotic control has promising capabilities such as inherent compliance and user safety. However, these are counterbalanced by issues not common to rigid robots, like nonlinear actuation dynamics, motion constraints, workspace limitations, and variable shape stiffness. In this work, we have adapted Model Predictive Control (MPC), that has recently seen an exponential rise in popularity and fields of applications, to a soft robotic arm called SoPrA. We have addressed the problems that current control methods are facing, trying to propose a unique environment to handle them in a modular way. This work shows, both with simulation and experimental results, that Task-Space MPC can be successfully implemented for dynamic soft robotic control, while past research has usually focused on Joint-Space references. We have provided a way to couple the Piece-wise Constant Curvature and the Augmented Rigid Body Model assumptions with internal and external constraints and actuation dynamics, delivering an algorithm that can manage all these information and optimize over them. We believe that an MPC implementation based on our approach could be the way to address most of model-based soft robotics control issues within a unified and modular framework, while allowing to include improvements that usually belong to other control domains such as learning techniques.

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“…In [1] the kinematic and dynamic models for SoPrA have been formulated, via PCC and Augmented Rigid Body [2]. The dynamic equation for the soft arm is defined as:…”

Section: A Mathematical Modelmentioning

confidence: 99%

“…2: Augmented Rigid Body Model: on the left the equivalent rigid joints and links, on the right the curvature-based space description. Adapted from [1].…”

Section: A Mathematical Modelmentioning

confidence: 99%

“…According to [1] the robotic arm is modeled with 2 curvature parameters per section, not the bending angle θ and off-plane rotation φ but rather two derived components:…”

Section: A Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Unified and Modular Model Predictive Control Framework for Soft Continuum Manipulators under Internal and External Constraints

Spinelli¹,

Katzschmann²

2022

Preprint

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Advanced Flexible Sensing Technologies for Soft Robots

Qu,

Cui,

et al. 2024

Adv Funct Materials

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Soft robots have recently attracted increasing interest due to their advantages in durability, flexibility, and deformability, which enable them to adapt to unstructured environments and perform various complex tasks. Perception is crucial for soft robots. To better mimic biological systems, sensors need to be integrated into soft robotic systems to obtain both proprioceptive and external perception for effective usage. This review summarizes the latest advancements in flexible sensing feedback technologies for soft robotic applications. It begins with an introduction to the development of various flexible sensors for soft robots, followed by an in‐depth exploration of smart materials and advanced manufacturing methods. A detailed description of flexible sensing modalities and methodologies is also included in the review to illustrate the continuous breakthrough of the technology. In addition, the applications of soft robots based on these advanced sensing technologies are concluded as well. The challenges of flexible sensing technologies for soft robots and promising solutions are finally discussed and analyzed to provide a prospect for future development. By examining the recent advances in intelligent flexible sensing technologies, this review is dedicated to highlighting the potential of soft robotics and motivating innovation within the field.

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Materializing Autonomy in Soft Robots across Scales

Berrueta,

Murphey,

Truby

2023

Advanced Intelligent Systems

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The impressive capabilities of living organisms arise from the way autonomy is materialized by their bodies. Across scales, living beings couple computational or cognitive intelligence with physical intelligence through body morphology, material multifunctionality, and mechanical compliance. While soft robotics has advanced the design and fabrication of physically intelligent bodies, the integration of information‐processing capabilities for computational intelligence remains a challenge. Consequently, perception and control limitations have constrained how soft robots are built today. Progress toward untethered autonomy will require deliberate convergence in how the field codevelops new materials, fabrication methods, and control strategies for soft robots. Here, a new perspective is put forward: that researchers should use tasks alone to impose material and information constraints on soft robot design. A conceptual framework is proposed for a task‐first design paradigm that sidesteps limitations imposed by control strategies. This framework allows emergent synergies between material and information processing properties of soft matter to be readily exploited for task‐capable agents. Particular attention is paid to the scale dependence of solutions. Finally, an outlook is presented on emerging research opportunities for achieving autonomy in future soft robots as large as elephant trunks and as small as paramecia.

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SoPrA: Fabrication & Dynamical Modeling of a Scalable Soft Continuum Robotic Arm with Integrated Proprioceptive Sensing

Cited by 35 publications

References 25 publications

A Unified and Modular Model Predictive Control Framework for Soft Continuum Manipulators under Internal and External Constraints

A Unified and Modular Model Predictive Control Framework for Soft Continuum Manipulators under Internal and External Constraints

Advanced Flexible Sensing Technologies for Soft Robots

Materializing Autonomy in Soft Robots across Scales

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