Design Optimization of a Pneumatic Soft Robotic Actuator Using Model-Based Optimization and Deep Reinforcement Learning

Raeisinezhad, Mahsa; Pagliocca, Nicholas; Koohbor, Behrad; Trkov, Mitja

doi:10.3389/frobt.2021.639102

Cited by 17 publications

(7 citation statements)

References 42 publications

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“…The actuator undergoes elongation when pressure is applied, and the extent of elongation is influenced by the pressure level within the undulated cavity. It is assumed that the tip of the actuator does not experience any deformation across its cross-section when subjected to pressurized fluid [ 28 , 29 ]. The resulting force causing the actuator to extend can be determined by considering the pressure applied to the actuator.…”

Section: Mathematical Modeling For Elongation Estimationmentioning

confidence: 99%

Quasi-Static Modeling Framework for Soft Bellow-Based Biomimetic Actuators

Heung,

Lei,

Liang

et al. 2024

Biomimetics

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Soft robots that incorporate elastomeric matrices and flexible materials have gained attention for their unique capabilities, surpassing those of rigid robots, with increased degrees of freedom and movement. Research has highlighted the adaptability, agility, and sensitivity of soft robotic actuators in various applications, including industrial grippers, locomotive robots, wearable assistive devices, and more. It has been demonstrated that bellow-shaped actuators exhibit greater efficiency compared to uniformly shaped fiber-reinforced actuators as they require less input pressure to achieve a comparable range of motion (ROM). Nevertheless, the mathematical quantification of the performance of bellow-based soft fluidic actuators is not well established due to their inherent non-uniform and complex structure, particularly when compared to fiber-reinforced actuators. Furthermore, the design of bellow dimensions is mostly based on intuition without standardized guidance and criteria. This article presents a comprehensive description of the quasi-static analytical modeling process used to analyze bellow-based soft actuators with linear extension. The results of the models are validated through finite element method (FEM) simulations and experimental testing, considering elongation in free space under fluidic pressurization. This study facilitates the determination of optimal geometrical parameters for bellow-based actuators, allowing for effective biomimetic robot design optimization and performance prediction.

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Section: Mathematical Modeling For Elongation Estimationmentioning

confidence: 99%

Quasi-Static Modeling Framework for Soft Bellow-Based Biomimetic Actuators

Heung,

Lei,

Liang

et al. 2024

Biomimetics

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“…34 Multiobjective goals balancing bending angle and contact force have been targeted through integrated FEA and optimization. [35][36][37][38] Overall, these pioneering studies have established the utility of modeling and analysis techniques for improving soft actuator designs.…”

Section: Introductionmentioning

confidence: 99%

A structural optimization analysis of cable-driven soft manipulator

Khalil,

Habib,

Seadby

et al. 2024

International Journal of Advanced Robotic Systems

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Cable-driven soft robots hold significant potential for surgical and industrial applications, yet their performance and maneuverability can be further enhanced through design optimization. By optimizing the design, factors such as bending angles, manipulator deformation, and overall functionality can be directly influenced, leading to improved interaction with the environment and more accurate task performance. This article presents a physics-based design optimization approach for cable-driven soft robotic manipulators, aiming to enhance bending performance through structural design enhancements. Four design criteria, namely, cross-sectional shape, material, gap shape, and gap size, are considered in the optimization process. Given the inherent nonlinearity of soft materials, finite element modeling techniques are employed to analyze the effects of modifying each design parameter on displacement and bending angle. The manipulator’s design is evaluated using ABAQUS/CAE, and an analysis of variance test is conducted to identify significant performance differences among the design parameters. The results reveal that material variation has the most substantial impact, followed by gap shape and gap size. Based on subsequent parameter optimization, Dragon Skin 10 is determined to be the optimal material for bending motion, while a trapezoidal gap shape is preferred. In addition, a genetic algorithm is utilized to select a maximum gap size of 8.87 mm. These findings provide valuable insights into key design principles for cable-driven soft manipulators, aiming to enhance flexibility and reduce actuation forces. By establishing a fundamental understanding of the relationship between morphology and motion capability, this methodology demonstrates an effective simulation-driven optimization approach that incorporates the nonlinear elastic behavior of materials to improve performance. Overall, this work establishes a framework for optimizing cable-driven architectures to suit various applications in the field of soft robotics.

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“…Instead, reinforcement learning learns how to move components around to get higher accumulative rewards based on the optimality of its solutions. Reinforcement learning, in particular, has been used for generative design 15 as well as design optimization 16 . Despite this increasing interest in using modern machine learning techniques for engineering design problems, to our knowledge, there has not been any work on using them for architecture synthesis.…”

Section: Introductionmentioning

confidence: 99%

A reinforcement learning approach to system modularization under constraints

Sanaei

Otto

Hölttä‐Otto

et al. 2023

Systems Engineering

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Modularization is an approach for system architecting and design simplification by encapsulating complex interactions among components within modules and reducing dependencies across modules. Design structure matrix (DSM) based clustering algorithms have proven helpful for such analysis, owing to their convenience in manipulating a large number of elements using conventional software. However, there are problems where constraints must be maintained in the modularization, for example, coping with functions or systems that either cannot or must be performed in regions with excessive heat, pressure, magnetic or other fields. Excluding such field boundary considerations can result in DSM computed modular architectural solutions that bundle field-incompatible functions or components that are not practical. Such regional field constraint considerations are not taken into account using conventional DSM clustering algorithms. We introduce a DSM-based clustering algorithm that incorporates these practical embodiment constraints through a constraint matrix indicating which elements can or cannot be placed in the same field region. We then employ reinforcement learning to allow the clustering algorithm to exploit its learnings from the previous attempts and during the clustering to facilitate the optimization under constraints. We demonstrate two examples of a medical contrast injector and the controller board of a three-phase pump motor.

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Design Optimization of a Pneumatic Soft Robotic Actuator Using Model-Based Optimization and Deep Reinforcement Learning

Cited by 17 publications

References 42 publications

Quasi-Static Modeling Framework for Soft Bellow-Based Biomimetic Actuators

Quasi-Static Modeling Framework for Soft Bellow-Based Biomimetic Actuators

A structural optimization analysis of cable-driven soft manipulator

A reinforcement learning approach to system modularization under constraints

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