Acoustic Sensing for Soft Pneumatic Actuators

Zöller, Gabriel; Wall, Vincent; Brock, Oliver

doi:10.1109/iros.2018.8594396

Cited by 36 publications

(17 citation statements)

References 24 publications

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“…Acoustic sensing relies on sound propagating through a soft continuum actuator to infer information about contact location, contact intensity, and contacted material [12], [13]. Since the required microphone and optional speaker can be embedded into the cavity of the pneumatic actuator, the effect on compliance is negligible.…”

Section: Related Workmentioning

confidence: 99%

A Low-Cost, Easy-to-Manufacture, Flexible, Multi-Taxel Tactile Sensor and its Application to In-Hand Object Recognition

Pannen¹,

Puhlmann²,

Brock³

2021

Preprint

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Soft robotics is an emerging field that yields promising results for tasks that require safe and robust interactions with the environment or with humans, such as grasping, manipulation, and human-robot interaction. Soft robots rely on intrinsically compliant components and are difficult to equip with traditional, rigid sensors which would interfere with their compliance. We propose a highly flexible tactile sensor that is low-cost and easy to manufacture while measuring contact pressures independently from 14 taxels. The sensor is built from piezoresistive fabric for highly sensitive, continuous responses and from a custom-designed flexible printed circuit board which provides a high taxel density. From these taxels, location and intensity of contact with the sensor can be inferred. In this paper, we explain the design and manufacturing of the proposed sensor, characterize its input-output relation, evaluate its effects on compliance when equipped to the silicone-based pneumatic actuators of the soft robotic RBO Hand 2, and demonstrate that the sensor provides rich and useful feedback for learning-based in-hand object recognition.

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Section: Related Workmentioning

confidence: 99%

A Low-Cost, Easy-to-Manufacture, Flexible, Multi-Taxel Tactile Sensor and its Application to In-Hand Object Recognition

Pannen¹,

Puhlmann²,

Brock³

2021

Preprint

Self Cite

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show abstract

“…4 Tactile sensors for soft robots. The figure shows photographs of tactile sensors implementations for soft robots, namely, the acoustic sensor for the PneuFlex actuator (Zöller et al 2018) (a) 1 and the GelSlim pressure sensing finger (Donlon et al 2018)…”

Section: Hand Controlmentioning

confidence: 99%

“…( 1 Reproduced with permission (Zöller et al 2018). Photo credit: Robotics and Biology Laboratory (RBO Lab.…”

Section: Hand Controlmentioning

confidence: 99%

Tactile Sensors

Parmiggiani¹,

Dussoni²,

Natale³

et al. 2020

Encyclopedia of Robotics

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“…A promising alternative to force sensing is to measure other quantities either tactile or related to other sensory sources -e.g. hand posture [15], audio signals [16], video streams [17] -and infer contact forces through algorithms. Model based techniques are hard to use in these cases, due to the well-known difficulties in formulating reliable models of the hand-object interaction when softness is involved [18].…”

Section: Introductionmentioning

confidence: 99%

To grasp or not to grasp: an end-to-end deep-learning approach for predicting grasping failures in soft hands

Arapi

Zhang

Averta

et al. 2020

2020 3rd IEEE International Conference on Soft Robotics (RoboSoft)

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This paper tackles the challenge of predicting grasp failures in soft hands before they happen, by combining deep learning with a sensing strategy based on distributed Inertial Measurement Units. We propose two neural architectures, which we implemented and tested with an articulated soft hand -the Pisa/IIT SoftHand -and a continuously deformable soft hand -the RBO Hand. The first architecture (Classifier) implements a-posteriori detection of the failure event, serving as a test-bench to assess the possibility of extracting failure information from the discussed input signals. This network reaches up to 100% of accuracy within our experimental validation. Motivated by these results, we introduce a second architecture (Predictor), which is the main contribution of the paper. This network works on-line and takes as input a multidimensional continuum stream of raw signals coming from the Inertial Measurement Units. The network is trained to predict the occurrence in the near future of a failure event.The Predictor detects 100% of failures with both hands, with the detection happening on average 1.96 seconds before the actual failing occurs -leaving plenty of time to an hypothetical controller to react.

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Acoustic Sensing for Soft Pneumatic Actuators

Cited by 36 publications

References 24 publications

A Low-Cost, Easy-to-Manufacture, Flexible, Multi-Taxel Tactile Sensor and its Application to In-Hand Object Recognition

A Low-Cost, Easy-to-Manufacture, Flexible, Multi-Taxel Tactile Sensor and its Application to In-Hand Object Recognition

Tactile Sensors

To grasp or not to grasp: an end-to-end deep-learning approach for predicting grasping failures in soft hands

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