Soft robotics has witnessed increased attention from the robotic community due to their desirable features in compliant manipulation in unstructured spaces and human-friendly applications. Their light-weight designs and low-stiffness are ideally suited for environments with fragile and sensitive objects without causing damage. Deformation sensing of soft robots so far has relied on highly nonlinear bending sensors and vision-based methods that are not suitable for obtaining precise and reliable state feedback. In this work, for the first time, we explore the use of a state-of-the-art high fidelity deformation sensor that is based on optical frequency domain reflcctometry in soft bending actuators. These sensors are capable of providing spatial coordinate feedback along the length of the sensor at every 0.8 mm at up to 250 Hz. This work systematically analyzes the sensor feedback for soft bending actuator deformation and then introduces a reduced order kinematic model, together with cubic spline interpolation, which could be used to reconstruct the continuous deformation of the soft bending actuators. The kinematic model is then extended to derive an efficient dynamic model which runs at 1.5 kHz and validated against the experimental data.
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Design and Fabrication of Sensorized Soft Effectors for Modular Soft Effectors
Biomimicr y is a i eld of study that involves imitating the designs and processes of nature to solve problems using man- made systems. Biomimicr y offer s an empathetic, interconnected under standing of how life wor ks and where we i t in. In bio- inspired designs, the main challenge is to develop a sustainable and effective fr amewor k that can be used in the real wor ld. M ost of the soft robotic designs are state-of-the-ar t models that cannot be used in real sensing applications. In this research, we propose a sustainable sensor-integr ated modular soft robot model that can be used for locomotion and gr ipping applications. This wor k presents the steps involved in modeling, designing, and fabr icating soft and l exible end effector s that can be used for soft robots with integr ated soft stretchable sensor s. We demonstr ate the design methodology involved in modeling and simulating the proposed model for robots that would require effector s with increased functionalities.
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- Award ID(s):
- 1924117
- PAR ID:
- 10451159
- Date Published:
- Journal Name:
- IEEE International Conference on Automation, Control and Robots
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Soft robots promise improved safety and capability over rigid robots when deployed near humans or in complex, delicate, and dynamic environments. However, infinite degrees of freedom and the potential for highly nonlinear dynamics severely complicate their modeling and control. Analytical and machine learning methodologies have been applied to model soft robots but with constraints: quasi-static motions, quasi-linear deflections, or both. Here, we advance the modeling and control of soft robots into the inertial, nonlinear regime. We controlled motions of a soft, continuum arm with velocities 10 times larger and accelerations 40 times larger than those of previous work and did so for high-deflection shapes with more than 110° of curvature. We leveraged a data-driven learning approach for modeling, based on Koopman operator theory, and we introduce the concept of the static Koopman operator as a pregain term in optimal control. Our approach is rapid, requiring less than 5 min of training; is computationally low cost, requiring as little as 0.5 s to build the model; and is design agnostic, learning and accurately controlling two morphologically different soft robots. This work advances rapid modeling and control for soft robots from the realm of quasi-static to inertial, laying the groundwork for the next generation of compliant and highly dynamic robots.
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