Design of a Biomimetic Tactile Sensor for Material Classification

Dai, Kevin; Wang, Xinyu; Rojas, Allison M.; Harber, Evan; Tian, Yu; Paiva, Nicholas; Gnehm, Joseph; Schindewolf, Evan; Choset, Howie; Webster‐Wood, Victoria A.; Li, Lü

doi:10.48550/arxiv.2203.15941

Cited by 2 publications

(3 citation statements)

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“…Different types of complex and customized tactile sensors were identified in literature which were used in different application and classification tasks. New proposed tactile sensors like Biomimetic tactile sensors, artificial mechanoreceptor tactile sensors, gridtype piezoelectric sensors, and ionics tactile sensors may be under a commercializing state and are not so quickly available [7,13,15,38,[40][41][42]. Based on the current state of the art, it is understood that artificial mechanoreceptors and tactile sensors share a common developmental base, detecting similar physical properties from stimuli.…”

Section: Identification Of Mechanoreceptor-inspired Tactile Sensorsmentioning

confidence: 99%

“…This research seeks to identify sensors capable of detecting tactile information with a performance comparable to that of human fingers, emphasizing cost-effectiveness. Previous research has primarily focused on utilizing customized tactile sensors that are expensive and application-specific [7][8][9][10][11][12][13]. The specialized architecture of these sensors limits their immediate deployment.…”

Section: Introductionmentioning

confidence: 99%

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Hardness Classification Using Cost-Effective Off-the-Shelf Tactile Sensors Inspired by Mechanoreceptors

Sharma,

Ferreira,

Justham

2024

Electronics

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Perception is essential for robotic systems, enabling effective interaction with their surroundings through actions such as grasping and touching. Traditionally, this has relied on integrating various sensor systems, including tactile sensors, cameras, and acoustic sensors. This study leverages commercially available tactile sensors for hardness classification, drawing inspiration from the functionality of human mechanoreceptors in recognizing complex object properties during grasping tasks. Unlike previous research using customized sensors, this study focuses on cost-effective, easy-to-install, and readily deployable sensors. The approach employs a qualitative method, using Shore hardness taxonomy to select objects and evaluate the performance of commercial off-the-shelf (COTS) sensors. The analysis includes data from both individual sensors and their combinations analysed using multiple machine learning approaches, and accuracy as the primary evaluation metric was considered. The findings illustrate that increasing the number of classification classes impacts accuracy, achieving 92% in binary classification, 82% in ternary, and 80% in quaternary scenarios. Notably, the performance of commercially available tactile sensors is comparable to those reported in the literature, which range from 50% to 98% accuracy, achieving 92% accuracy with a limited data set. These results highlight the capability of COTS tactile sensors in hardness classification giving accuracy levels of 92%, while being cost-effective and easier to deploy than customized tactile sensors.

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Section: Identification Of Mechanoreceptor-inspired Tactile Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hardness Classification Using Cost-Effective Off-the-Shelf Tactile Sensors Inspired by Mechanoreceptors

Sharma,

Ferreira,

Justham

2024

Electronics

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“…The grasper was fitted with two sensors: 1) a 9-degree of freedom inertial measurement unit (BNO055) to sense absolute orientation of the grasper, and 2) a Hall-effect-based soft magnetic force sensor [4,7] to measure the grasper's closing pressure. A laser time-of-flight sensor (VL53L5CX, SparkFun ® ) was mounted externally and pointed at the odontophore outer shell to obtain positional feedback.…”

Section: Odontophore Design and Fabricationmentioning

confidence: 99%

SLUGBOT, an Aplysia-Inspired Robotic Grasper for Studying Control

Dai

Sukhnandan

Bennington

et al. 2022

Biomimetic and Biohybrid Systems

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Living systems can use a single periphery to perform a variety of tasks and adapt to a dynamic environment. This multifunctionality is achieved through the use of neural circuitry that adaptively controls the reconfigurable musculature. Current robotic systems struggle to flexibly adapt to unstructured environments. Through mimicry of the neuromechanical coupling seen in living organisms, robotic systems could potentially achieve greater autonomy. The tractable neuromechanics of the sea slug Aplysia californica's feeding apparatus, or buccal mass, make it an ideal candidate for applying neuromechanical principles to the control of a soft robot. In this work, a robotic grasper was designed to mimic specific morphology of the Aplysia feeding apparatus. These include the use of soft actuators akin to biological muscle, a deformable grasping surface, and a similar muscular architecture. A previously developed Boolean neural controller was then adapted for the control of this soft robotic system. The robot was capable of qualitatively replicating swallowing behavior by cyclically ingesting a plastic tube. The robot's normalized translational and rotational kinematics of the odontophore followed profiles observed in vivo despite morphological differences. This brings Aplysia-inspired control in roboto one step closer to multifunctional neural control schema in vivo and in silico. Future additions may improve SLUGBOT's viability as a neuromechanical research platform.

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Design of a Biomimetic Tactile Sensor for Material Classification

Cited by 2 publications

References 0 publications

Hardness Classification Using Cost-Effective Off-the-Shelf Tactile Sensors Inspired by Mechanoreceptors

Hardness Classification Using Cost-Effective Off-the-Shelf Tactile Sensors Inspired by Mechanoreceptors

SLUGBOT, an Aplysia-Inspired Robotic Grasper for Studying Control

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