Evaluation Environment for Control Design of Soft Pneumatic Actuators

Krause, Jan Christoph; Ibrahim, Serhat; Raatz, Annika

doi:10.1007/978-3-662-59317-2_8

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…The identification process of these parameters is also mentioned in Krause et al (2019) . In addition to the procedure mentioned above, an identification of the actuator geometry is carried out.…”

Section: Identificationmentioning

confidence: 99%

Modeling and Reconstruction of State Variables for Low-Level Control of Soft Pneumatic Actuators

Ibrahim¹,

Krause²,

Olbrich³

et al. 2021

Front. Robot. AI

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To further advance closed-loop control for soft robotics, suitable sensor and modeling strategies have to be investigated. Although there are many flexible and soft sensors available, the integration into the actuator and the use in a control loop is still challenging. Therefore, a state-space model for closed-loop low-level control of a fiber-reinforced actuator using pressure and orientation measurement is investigated. To do so, the integration of an inertial measurement unit and geometric modeling of actuator is presented. The piecewise constant curvature approach is used to describe the actuator’s shape and deformation variables. For low-level control, the chamber’s lengths are reconstructed from bending angles with a geometrical model and the identified material characteristics. For parameter identification and model validation, data from a camera tracking system is analyzed. Then, a closed-loop control of pressure and chambers’ length of the actuator is investigated. It will be shown, that the reconstruction model is suitable for estimating the state variables of the actuator. In addition, the use of the inertial measurement unit will demonstrate a cost-effective and compact sensor for soft pneumatic actuators.

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Section: Identificationmentioning

confidence: 99%

Modeling and Reconstruction of State Variables for Low-Level Control of Soft Pneumatic Actuators

Ibrahim¹,

Krause²,

Olbrich³

et al. 2021

Front. Robot. AI

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the focus of the paper was on a new specific type of soft robot actuator design while our paper is focused on the development of general soft robot actuator metrics. Similarly, Krause et al (2019) developed a test bed for testing and evaluating Fluidic Elastic Actuators. However, this test bed does not appear to generalize well to any other type of soft robot actuator so that its application is limited.…”

Section: Introductionmentioning

confidence: 99%

Performance Metrics for Fluidic Soft Robot Rotational Actuators

2021

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The field of soft robotics is continuing to grow as more researchers see the potential for robots that can safely interact in unmodeled, unstructured, and uncertain environments. However, in order for the design, integration, and control of soft robotic actuators to develop into a full engineering methodology, a set of metrics and standards need to be established. This paper attempts to lay the groundwork for that process by proposing six soft robot actuator metrics that can be used to evaluate and compare characteristics and performance of soft robot actuators. Data from eight different soft robot rotational actuators (five distinct designs) were used to evaluate these soft robot actuator metrics and show their utility. Additionally we provide a simple case study as an example of how these metrics can be used to evaluate soft robot actuators for a designated task. While this paper does not claim to present a comprehensive list of all possible soft robot actuator metrics, the metrics presented can 1) be used to initiate the development and comparison of soft robot actuators in an engineering framework and 2) start a broader discussion of which metrics should be standardized in future soft robot actuator research.

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Evaluation Environment for Control Design of Soft Pneumatic Actuators

Cited by 2 publications

References 20 publications

Modeling and Reconstruction of State Variables for Low-Level Control of Soft Pneumatic Actuators

Modeling and Reconstruction of State Variables for Low-Level Control of Soft Pneumatic Actuators

Performance Metrics for Fluidic Soft Robot Rotational Actuators

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