robotic materials

Interview with Nikolaus Correll: Robotic Materials

Anonymous — Mon, 20 Dec 2021 07:00:00 +0000

Interview with Nikolaus Correll: Robotic Materials Anonymous (not verified) Mon, 12/20/2021 - 00:00

NC: I see myself as a proponent of computation in the field of smart materials. But the word ‘smart’ is overloaded; there is also a lot of discussion about active materials. I created the term ‘robotic materials’ because I come from robotics and my research is on materials for robots. Robots are placed in the real world and must therefore respond to an often uncertain environment. Robotic materials can take an active part in this challenge since they integrate acting and reacting into the material itself [Mengüç et al. 2017]. I think a robotic material is the ultimate smart material because it actually behaves like a robot–for example, it can move, it can change its shape, and its appearance [Hughes, Heckman, and Correll 2019]. It is about a new class of metamaterials that tightly integrate sensing, actuation, computation, communication, and power routing in a periodic fashion.

Reference

Correll, Nikolaus, Michael Friedman, and Karin Krauthausen. "Interview with Nikolaus Correll: Robotic Materials." Active Materials (2021): 173. [PDF]

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Miniaturized circuitry for capacitive self-sensing and closed-loop control of soft electrostatic transducers

Anonymous — Wed, 01 Dec 2021 07:00:00 +0000

Miniaturized circuitry for capacitive self-sensing and closed-loop control of soft electrostatic transducers Anonymous (not verified) Wed, 12/01/2021 - 00:00

Soft robotics is a field of robotic system design characterized by materials and structures that exhibit large-scale deformation, high compliance, and rich multifunctionality. The incorporation of soft and deformable structures endows soft robotic systems with the compliance and resiliency that makes them well-adapted for unstructured and dynamic environments. While actuation mechanisms for soft robots vary widely, soft electrostatic transducers such as dielectric elastomer actuators (DEAs) and hydraulically amplified self-healing electrostatic (HASEL) actuators have demonstrated promise due to their muscle-like performance and capacitive selfsensing capabilities. Despite previous efforts to implement self-sensing in electrostatic transducers by overlaying sinusoidal low-voltage signals, these designs still require sensing high-voltage signals, requiring bulky components that prevent integration with miniature, untethered soft robots. We present a circuit design that eliminates the need for any high-voltage sensing components, thereby facilitating the design of simple, low cost circuits using off-the-shelf components. Using this circuit, we perform simultaneous sensing and actuation for a range of electrostatic transducers including circular DEAs and HASEL actuators and demonstrate accurate estimated displacements with errors under 4%. We further develop this circuit into a compact and portable system that couples HV actuation, sensing, and computation as a prototype towards untethered, multifunctional soft robotic systems. Finally, we demonstrate the capabilities of our self-sensing design through feedback-control of a robotic arm powered by Peano-HASEL actuators.

Reference

Ly, K., Kellaris, N., McMorris, D., Johnson, B.K., Acome, E., Sundaram, V., Naris, M., Humbert, J.S., Rentschler, M.E., Keplinger, C. and Correll, N., 2021. Miniaturized circuitry for capacitive self-sensing and closed-loop control of soft electrostatic transducers. Soft Robotics, 8(6), pp.673-686. [PDF]

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Embedded Neural Networks for Robot Autonomy

Anonymous — Fri, 01 Nov 2019 06:00:00 +0000

Embedded Neural Networks for Robot Autonomy Anonymous (not verified) Fri, 11/01/2019 - 00:00

We present a library to automatically embed signal processing and neural network predictions into the material robots are made of. Deep and shallow neural network models are first trained offline using state-of-the-art machine learning tools and then transferred onto general purpose microcontrollers that are co-located with a robot's sensors and actuators. We validate this approach using multiple examples: a smart robotic tire for terrain classification, a robotic finger sensor for load classification and a smart composite capable of regressing impact source localization. In each example, sensing and computation are embedded inside the material, creating artifacts that serve as stand-in replacement for otherwise inert conventional parts. The open source software library takes as inputs trained model files from higher level learning software, such as Tensorflow/Keras, and outputs code that is readable in a microcontroller that supports C. We compare the performance of this approach for various embedded platforms. In particular, we show that low-cost off-the-shelf microcontrollers can match the accuracy of a desktop computer, while being fast enough for real-time applications at different neural network configurations. We provide means to estimate the maximum number of parameters that the hardware will support based on the microcontroller's specifications.

Website

www.nn4mc.com

Reference

Patel, Radhen, Christoffer Heckman, and Nikolaus Correll. "Embedded Neural Networks for Robot Autonomy." In Robotics Research: The 19th International Symposium ISRR, vol. 20, p. 242. Springer Nature, 2022. [Link]

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