Design Approach for Heavy-Duty Soft-Robotic-Gripper

Müller, Alexander; Aydemir, Muhammed; Glodde, Arne; Dietrich, Franz

doi:10.1016/j.procir.2020.02.180

Cited by 13 publications

(6 citation statements)

References 19 publications

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“…The bending of the soft actuator is regulated by two asymmetric pressure chambers as demonstrated by FEA simulation models in Figure 2. FEA has been widely employed in numerous studies that focus on soft robot design performance [94,95]. For instance, Nguyen et al [96] investigated the design of fabric soft pneumatic actuators for wearable assistive devices using FEA for performance analysis.…”

Section: Digital Engineeringmentioning

confidence: 99%

Soft Robot Design, Manufacturing, and Operation Challenges: A Review

Ambaye,

Boldsaikhan,

Krishnan

2024

JMMP

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Advancements in smart manufacturing have embraced the adoption of soft robots for improved productivity, flexibility, and automation as well as safety in smart factories. Hence, soft robotics is seeing a significant surge in popularity by garnering considerable attention from researchers and practitioners. Bionic soft robots, which are composed of compliant materials like silicones, offer compelling solutions to manipulating delicate objects, operating in unstructured environments, and facilitating safe human–robot interactions. However, despite their numerous advantages, there are some fundamental challenges to overcome, which particularly concern motion precision and stiffness compliance in performing physical tasks that involve external forces. In this regard, enhancing the operation performance of soft robots necessitates intricate, complex structural designs, compliant multifunctional materials, and proper manufacturing methods. The objective of this literature review is to chronicle a comprehensive overview of soft robot design, manufacturing, and operation challenges in conjunction with recent advancements and future research directions for addressing these technical challenges.

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Section: Digital Engineeringmentioning

confidence: 99%

Soft Robot Design, Manufacturing, and Operation Challenges: A Review

Ambaye,

Boldsaikhan,

Krishnan

2024

JMMP

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“…1, c) suitable for multiple shapes and object types can often be assigned to the field of soft robotics. Typical examples are usages of adaptable surfaces, such as FinRays [9][10][11], often in combination with other gripping principles such as mechanical and electrostatic mechanisms [12]. One of these soft robotic applications, that has been gaining traction in the past few years, are robotic grippers based on the jamming of granular materials [13,14].…”

Section: State Of the Artmentioning

confidence: 99%

Empirically adapted model for the Handling of Variable Geometries with Vacuum-based Granulate Grippers

Wacker¹,

Dierks²

2022

Preprint

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<p> Current industrial trends show an increasing demand for individualized products, which require highly flexible yet automated production systems. Universal handling systems offer an efficient solution for the flexible and safe handling of differing component geometries and shapes. An innovative gripper for form-flexible handling combining vacuum systems with the flexibility of granulate grippers was established in previous research and has continued to prove its flexibility by gripping wide varieties of object geometries. The current challenge of modelling and predicting gripping forces for this new gripper is addressed in this research. Multiple object geometries are selected and examined, with the parameters affecting the air permeability being the most important influence for the gripping forces. Along with an overview of influencing factors and parameters, a framework for a linear model enabling the prediction of gripping forces for different object shapes is developed. The basis for automated prediction of gripping strengths for different types of objects is established with this research and could be adapted with other, non-analytical models such as machine learning in the future. </p>

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“…Many authors opted for optimization in the design stage itself, to obtain better performance of the developed robotic grippers. Müller et al [25] describe several approaches for the design of a soft robotic gripper including selection of the appropriate model for characterization of the maximum payload on the gripper, material characterization, and simulation of grasp to determine its performance at various loads. Kim et al [26] developed a Deep-Neural-Network-based algorithm to identify optimal grasping points based on an object's geometric features for obtaining a stable grasp.…”

Section: Review Of Previous Workmentioning

confidence: 99%

Study of grasp-energy based optimal distribution of contact forces on a humanoid robotic hand during object grasp

et al. 2021

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A novel grasp optimization algorithm for minimizing the net energy utilized by a five-fingered humanoid robotic hand with twenty degrees of freedom for securing a precise grasp is presented in this study. The algorithm utilizes a compliant contact model with a nonlinear spring and damper system to compute the performance measure, called ‘Grasp Energy’. The measure, subject to constraints, has been minimized to obtain locally optimal cartesian trajectories for securing a grasp. A case study is taken to compare the analytical (applying the optimization algorithm) and the simulated data in MSC.Adams $^{^{\circledR}}$ , to prove the efficacy of the proposed formulation.

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Design Approach for Heavy-Duty Soft-Robotic-Gripper

Cited by 13 publications

References 19 publications

Soft Robot Design, Manufacturing, and Operation Challenges: A Review

Soft Robot Design, Manufacturing, and Operation Challenges: A Review

Empirically adapted model for the Handling of Variable Geometries with Vacuum-based Granulate Grippers

Study of grasp-energy based optimal distribution of contact forces on a humanoid robotic hand during object grasp

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