Robust Distance Estimation of Capacitive Proximity Sensors in HRI using Neural Networks

Poeppel, Alexander; Hoffmann, Alwin; Siehler, Martin; Reif, Wolfgang

doi:10.1109/irc.2020.00061

Cited by 9 publications

(6 citation statements)

References 21 publications

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“…The prediction errors for distance and angle are in line with other studies. For the distance prediction, the approach of Poeppel et al [9] has been outperformed for distances with less than 200 mm. They reported an average error of 20.7 mm with a detection range of 350 mm.…”

Section: Discussionmentioning

confidence: 99%

“…Hoffmann et al [8] developed an environment-aware capacitive sensor and used a nearest neighbor search to estimate the distance to obstacles based on an environment model. Poeppel et al [9] used a neural network to compensate external influences on capacitive sensors for robust distance estimation. Erickson et al [10] used a neural network to estimate the relative position and pitch and yaw from a series of capacitive sensor readings.…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, labeling data is expensive and prone to errors. Common capacitive sensor systems are typically equipped with microcontrollers with limited code and data memory and operating frequencies in the mid to low MHz range, such as Arduino [9] or Teensy [10]. To maintain a constant execution time of the system, it is important to keep the computational cost as low as possible.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Smart Capacitive Sensor Skin with Embedded Data Quality Indication for Enhanced Safety in Human–Robot Interaction

Scholl

Tobola

Ludwig

et al. 2021

Sensors

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Smart sensors are an integral part of the Fourth Industrial Revolution and are widely used to add safety measures to human–robot interaction applications. With the advancement of machine learning methods in resource-constrained environments, smart sensor systems have become increasingly powerful. As more data-driven approaches are deployed on the sensors, it is of growing importance to monitor data quality at all times of system operation. We introduce a smart capacitive sensor system with an embedded data quality monitoring algorithm to enhance the safety of human–robot interaction scenarios. The smart capacitive skin sensor is capable of detecting the distance and angle of objects nearby by utilizing consumer-grade sensor electronics. To further acknowledge the safety aspect of the sensor, a dedicated layer to monitor data quality in real-time is added to the embedded software of the sensor. Two learning algorithms are used to implement the sensor functionality: (1) a fully connected neural network to infer the position and angle of objects nearby and (2) a one-class SVM to account for the data quality assessment based on out-of-distribution detection. We show that the sensor performs well under normal operating conditions within a range of 200 mm and also detects abnormal operating conditions in terms of poor data quality successfully. A mean absolute distance error of 11.6mm was achieved without data quality indication. The overall performance of the sensor system could be further improved to 7.5mm by monitoring the data quality, adding an additional layer of safety for human–robot interaction.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Smart Capacitive Sensor Skin with Embedded Data Quality Indication for Enhanced Safety in Human–Robot Interaction

Scholl

Tobola

Ludwig

et al. 2021

Sensors

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“…In [25], a detection distance of about 30 cm for electrodes of size between 50 × 50 mm 2 and 100×100 mm 2 is reported for HRI (AT-I). Finally, as the sensing range increases, self-influence becomes an issue that needs to be addressed [11], [50].…”

Section: A Capacitive Sensingmentioning

confidence: 99%

Proximity Perception in Human-Centered Robotics: A Survey on Sensing Systems and Applications

et al. 2022

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“…This is impractical due to 1) the high cost of ToF sensors such as lidar or millimeter wave radar, and 2) large numbers of of ToF sensors emitting at the same time will interfere with each other, which complicates system design. Finally, other type of sensing modalities such as infrared sensors [3] or capacitive coupling sensors [10] which leverage signal interference patterns have been proposed for on-robot collision avoidance. However, these sensors are orientation-dependent, and capacitive sensing only works on obstacles that have purposely calibrated dielectric constants, such as water or human tissues.…”

Section: Related Workmentioning

confidence: 99%

AuraSense: Robot Collision Avoidance by Full Surface Proximity Detection

Fan¹,

Simmons-Edler²,

Lee³

et al. 2021

Preprint

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Perceiving obstacles and avoiding collisions is fundamental to the safe operation of a robot system, particularly when the robot must operate in highly dynamic human environments. Proximity detection using on-robot sensors can be used to avoid or mitigate impending collisions. However, existing proximity sensing methods are orientation and placement dependent, resulting in blind spots even with large numbers of sensors. In this paper, we introduce the phenomenon of the Leaky Surface Wave (LSW), a novel sensing modality, and present AuraSense, a proximity detection system using the LSW. AuraSense is the first system to realize no-dead-spot proximity sensing for robot arms. It requires only a single pair of piezoelectric transducers, and can easily be applied to off-theshelf robots with minimal modifications. We further introduce a set of signal processing techniques and a lightweight neural network to address the unique challenges in using the LSW for proximity sensing. Finally, we demonstrate a prototype system consisting of a single piezoelectric element pair on a robot manipulator, which validates our design. We conducted several micro benchmark experiments and performed more than 2000 on-robot proximity detection trials with various potential robot arm materials, colliding objects, approach patterns, and robot movement patterns. AuraSense achieves 100% and 95.3% true positive proximity detection rates when the arm approaches static and mobile obstacles respectively, with a true negative rate over 99%, showing the real-world viability of this system.

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Robust Distance Estimation of Capacitive Proximity Sensors in HRI using Neural Networks

Cited by 9 publications

References 21 publications

A Smart Capacitive Sensor Skin with Embedded Data Quality Indication for Enhanced Safety in Human–Robot Interaction

A Smart Capacitive Sensor Skin with Embedded Data Quality Indication for Enhanced Safety in Human–Robot Interaction

Proximity Perception in Human-Centered Robotics: A Survey on Sensing Systems and Applications

AuraSense: Robot Collision Avoidance by Full Surface Proximity Detection

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