Embedded Magnetic Sensing for Feedback Control of Soft HASEL Actuators

Sundaram, Vani; Ly, Khoi; Johnson, Brian K.; Naris, Mantas; Anderson, Maxwell P.; Humbert, J. Sean; Correll, Nikolaus; Rentschler, Mark E.

doi:10.1109/tro.2022.3200164

Cited by 8 publications

(14 citation statements)

References 62 publications

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“…The physical soft actuator systems studied in this work are: (1) A tendon-based soft actuator with an internal network of optical sensors and a Tensilica Xtensa LX6 (ESP32 Thing, Sparkfun), 10 and (2) A folded HASEL-based, multilayered platform equipped with magnetic sensors and an ARM Cortex-M4f platform (Teensy 3.6, PJRC). 9 The tendon-based platform in Figure 1(a) consists of a soft 3D-printed mesh that has a network of embedded LightLace stretchable sensors, organically weaved inside the soft mesh structure by hand during fabrication. The endeffector connects to two high torque servos (Dynamixel RX-64, Robotis) using Kevlar threads (Kevlar Fiber, Dupont) that serve as tendons.…”

Section: Hardware Testbedsmentioning

confidence: 99%

“…The magnetic block is a mixture of platinum-catalyzed silicone (Ecoflex 00-30, Smooth On) and neo-powder (NQB-B+20441, Neo Magnequench), with a total mass of around 10 g and dimensions of 50 × 50 × 5 mm 3 . Sundaram et al 9 describes in more detail the process of manufacturing these sensors. We sandwich the HASEL unit between the magnetic block on top and the magnetometer at the bottom.…”

Section: Sensing and Actuationmentioning

confidence: 99%

“…Throughout this work, we use the term "untethered" interchangeably with the term "computationally untethered," i.e., high-performance computation in the firmware of the embedded low-power compute units or units without being tethered to personal computers, clouds, joysticks, or any remote brains and using feedback from proprioception provided by networks of synthetic afferent sensor networks. 9 Aguasvivas Manzano et al 10 presents initial formulations and a Python prototype of the output tracking technique developed in this work for the tendon-based platform with embedded LightLace sensors developed by Xu et al we also study the robustness of this control method against mechanical disturbances. Figure 1(a) displays the actuating, tendon-based, platform that uses feedback from this network of embedded LightLace sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Throughout this work, we use the term “untethered” interchangeably with the term “computationally untethered,” i.e., high-performance computation in the firmware of the embedded low-power compute units or units without being tethered to personal computers, clouds, joysticks, or any remote brains and using feedback from proprioception provided by networks of synthetic afferent sensor networks. 9…”

Section: Introductionmentioning

confidence: 99%

“…(A) A tendon-based platform with embedded LightLace sensors 10 (B) A HASEL-based tilting platform with embedded magnetic sensors. 9 signals and actuates the HASELs, but also does all the computation for high-bandwidth neural-based feedback control. Figure 1(b) pictures this platform containing magnetic sensors 9 and HASEL actuators.…”

Section: Introductionmentioning

confidence: 99%

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Toward smart composites: Small-scale, untethered prediction and control for soft sensor/actuator systems

Manzano

Sundaram

et al. 2022

Journal of Composite Materials

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We present formulation and open-source tools to achieve in-material model predictive control of sensor/actuator systems using learned forward kinematics and on-device computation. Microcontroller units that compute the prediction and control task while colocated with the sensors and actuators enable in-material untethered behaviors. In this approach, small parameter size neural network models learn forward kinematics offline. Our open-source compiler, nn4mc, generates code to offload these predictions onto MCUs. A Newton-Raphson solver then computes the control input in real time. We first benchmark this nonlinear control approach against a PID controller on a mass-spring-damper simulation. We then study experimental results on two experimental rigs with different sensing, actuation and computational hardware: a tendon-based platform with embedded LightLace sensors and a HASEL-based platform with magnetic sensors. Experimental results indicate effective high-bandwidth tracking of reference paths (≥120 Hz) with a small memory footprint (≤6.4% of flash memory). The measured path following error does not exceed 2mm in the tendon-based platform. The simulated path following error does not exceed 1mm in the HASEL-based platform. The mean power consumption of this approach in an ARM Cortex-M4f device is 45.4 mW. This control approach is also compatible with Tensorflow Lite models and equivalent on-device code. In-material intelligence enables a new class of composites that infuse autonomy into structures and systems with refined artificial proprioception.

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Section: Hardware Testbedsmentioning

confidence: 99%

Section: Sensing and Actuationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Toward smart composites: Small-scale, untethered prediction and control for soft sensor/actuator systems

Manzano

Sundaram

et al. 2022

Journal of Composite Materials

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A multifunctional soft robotic shape display with high-speed actuation, sensing, and control

Johnson,

Naris,

Sundaram

et al. 2023

Nat Commun

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Shape displays which actively manipulate surface geometry are an expanding robotics domain with applications to haptics, manufacturing, aerodynamics, and more. However, existing displays often lack high-fidelity shape morphing, high-speed deformation, and embedded state sensing, limiting their potential uses. Here, we demonstrate a multifunctional soft shape display driven by a 10 × 10 array of scalable cellular units which combine high-speed electrohydraulic soft actuation, magnetic-based sensing, and control circuitry. We report high-performance reversible shape morphing up to 50 Hz, sensing of surface deformations with 0.1 mm sensitivity and external forces with 50 mN sensitivity in each cell, which we demonstrate across a multitude of applications including user interaction, image display, sensing of object mass, and dynamic manipulation of solids and liquids. This work showcases the rich multifunctionality and high-performance capabilities that arise from tightly-integrating large numbers of electrohydraulic actuators, soft sensors, and controllers at a previously undemonstrated scale in soft robotics.

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