Noise-Tolerant Identification and Tuning Approach Using Deep Neural Networks for Visual Servoing Applications

Hay, Oussama Abdul; Chehadeh, Mohamad; Ayyad, Abdulla; Wahbah, Mohamad; Humais, Muhammad Ahmed; Boiko, Igor; Seneviratne, Lakmal; Zweiri, Yahya

doi:10.1109/tro.2023.3235586

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…The pixel-wise intensity changes, called events or spikes, are recorded at a temporal resolution on the order of microseconds. Event-based cameras have been applied in autonomous drone racing [38], space imaging [39], space exploration [40], automated drilling [41], and visual servoing [42,43]. Neuromorphic cameras' fast update rate, along with their high dynamic range (140 dB compared to conventional cameras with 60 dB [44]) and low power consumption, make them apt for robotics tasks [45].…”

Section: Neuromorphic Vision-based Tactile Sensingmentioning

confidence: 99%

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

Sajwani¹,

Ayyad²,

Alkendi³

et al. 2023

Preprint

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Vision-based tactile sensors (VBTS) have become the de facto method of giving robots the ability to obtain tactile feedback from their environment. Unlike other solutions to tactile sensing, VBTS offers high spatial resolution feedback without compromising on instrumentation costs or incurring additional maintenance expenses. However, conventional cameras used in VBTS have a fixed update rate and output redundant data, leading to computational overhead downstream. In this work, we present a neuromorphic vision-based tactile sensor (N-VBTS) that employs observations from an event-based camera for contact angle prediction. Particularly, we design and develop a novel graph neural network, dubbed TactiGraph, that asynchronously operates on graphs constructed from raw N-VBTS streams exploiting their spatiotemporal correlations to perform predictions. Although conventional VBTS uses an internal illumination source, TactiGraph is reported to perform efficiently in both scenarios, with and without an internal illumination source. Rigorous experimental results revealed that TactiGraph achieved a mean absolute error of 0.62∘ in predicting the contact angle and was faster and more efficient than both conventional VBTS and other N-VBTS, with lower instrumentation costs. Specifically, N-VBTS requires only 5.5% of the compute-time needed by VBTS when both are tested on the same scenario.

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Section: Neuromorphic Vision-based Tactile Sensingmentioning

confidence: 99%

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

Sajwani¹,

Ayyad²,

Alkendi³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The pixel-wise intensity changes, called events or spikes, are recorded at a temporal resolution on the order of microseconds. Event-based cameras have been applied in autonomous drone racing [39], space imaging [40], space exploration [41], automated drilling [42], and visual servoing [43,44]. Neuromorphic cameras' fast update rate, along with their high dynamic range (140 dB compared to conventional cameras with 60 dB [45]) and low power consumption, make them apt for robotics tasks [46].…”

Section: Neuromorphic Vision-based Tactile Sensingmentioning

confidence: 99%

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

Sajwani

Ayyad

Alkendi

et al. 2023

Sensors

Self Cite

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Vision-based tactile sensors (VBTSs) have become the de facto method for giving robots the ability to obtain tactile feedback from their environment. Unlike other solutions to tactile sensing, VBTSs offer high spatial resolution feedback without compromising on instrumentation costs or incurring additional maintenance expenses. However, conventional cameras used in VBTS have a fixed update rate and output redundant data, leading to computational overhead.In this work, we present a neuromorphic vision-based tactile sensor (N-VBTS) that employs observations from an event-based camera for contact angle prediction. In particular, we design and develop a novel graph neural network, dubbed TactiGraph, that asynchronously operates on graphs constructed from raw N-VBTS streams exploiting their spatiotemporal correlations to perform predictions. Although conventional VBTSs use an internal illumination source, TactiGraph is reported to perform efficiently in both scenarios (with and without an internal illumination source) thus further reducing instrumentation costs. Rigorous experimental results revealed that TactiGraph achieved a mean absolute error of 0.62∘ in predicting the contact angle and was faster and more efficient than both conventional VBTS and other N-VBTS, with lower instrumentation costs. Specifically, N-VBTS requires only 5.5% of the computing time needed by VBTS when both are tested on the same scenario.

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“…Sommerhalder, M., Wolf, P., Riener, R., and Hutter, M., ANYexo 2.0: A Fully Actuated Upper-Limb Exoskeleton for Manipulation and Joint-Oriented Training in All Stages of Rehabilitation; TRO June 2023 2131-2150 Zimmermann, Y.,Song, J., Deguelle, C., Laderach, J., Zhou, L., Hutter, M., Riener, R., and Wolf, P., Human-Robot Attachment System for Exoskeletons: Design and Performance Analysis; TRO Aug. 2023 3087-3105 Zong, L.,Liang, G., and Lam, T.L., Kinematics Modeling and Control of2276-2288, DECEMBER 2023 + Check author entry for coauthors Reinforced Actuator With Tunable Attitude Ability for Grasping. Jiang, P., +, TRO April 2023 1164-1177 Active-Cooling-in-the-Loop Controller Design and Implementation for an SMA-Driven Soft Robotic Tentacle.…”

mentioning

confidence: 99%

2023 Index IEEE Transactions on Robotics Vol. 39

2023

IEEE Trans. Robot.

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This index covers all technical items-papers, correspondence, reviews, etc.-that appeared in this periodical during 2023, and items from previous years that were commented upon or corrected in 2023. Departments and other items may also be covered if they have been judged to have archival value.The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index.

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Noise-Tolerant Identification and Tuning Approach Using Deep Neural Networks for Visual Servoing Applications

Cited by 7 publications

References 36 publications

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

2023 Index IEEE Transactions on Robotics Vol. 39

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