Design of a Tactile Sensor for Robot Hands

Cannata, Giorgio; Maggiali, Marco

doi:10.5772/6626

Cited by 10 publications

(10 citation statements)

References 27 publications

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“…Thus, SDS registers the slip signal if the clamping force value of the gripper sponges does not correspond to the manipulated object mass. So, slip displacement signal can be registered by various methods, let's consider the main ones [9,10,[13][14][15][16][17][18][19][20][21][22].…”

Section: The Slippage Signal Detection In Robotic Systemsmentioning

confidence: 99%

“…Fixing sensitive elements on the manipulated object. The method registers the sensitive elements displacement relative to the gripper when object is slipping [19].…”

Section: The Slippage Signal Detection In Robotic Systemsmentioning

confidence: 99%

“…Registration configuration changes sensitive elements. The method is based on the registration of parameters of elastic sensitive elements (eg, conductive rubber) when their configuration and electrical resistance change [13,19,20,22].…”

Section: The Slippage Signal Detection In Robotic Systemsmentioning

confidence: 99%

“…Sensor systems based on conductive rubber are widely used in modern adaptive robotic sensing systems [19,20,[23][24][25]. For example, in contrast to capacitive sensors, such devices are significantly less sensitive to environmental influences, including electromagnetic fields.…”

Section: Conductive Rubber Elements In Robotic Measurement Systemsmentioning

confidence: 99%

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A Simulation Model for Robot’s Slip Displacement Sensors

Kondratenko¹,

Gerasin²,

Topalov³

2016

IJC

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This paper deals with a simulation model of slip displacement sensors for the object slip signals’ registration in the adaptive robot’s gripper. The study presents the analysis of different methods for slip displacement signals detection, as well as authors’ solutions. Special attention is paid to the investigations of the developed sensor with the resistive registration element in rod type structure of sensitive elements, which is able to operate in harsh and corrosive environments. A sensing system for the object slip signals’ registration in the adaptive robot’s gripper with a clamping force correction is developed for proposed slip displacement sensor with multi-component resistive registration elements. The hardware implementation of the sensing system for slip signals’ registration and obtained results are considered in details. The simulation model of the proposed slip displacement sensor based on polytypic conductive rubber is modeled by Proteus software. The intelligent approaches with the use of a field programmable gate array (FPGA) and VHDL-model to the sensing system designing allow to define the slippage direction in slip displacement sensor based on resistive registration elements. Thus, this expands the functionality of the developed sensor.

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Section: The Slippage Signal Detection In Robotic Systemsmentioning

confidence: 99%

“…Fixing sensitive elements on the manipulated object. The method registers the sensitive elements displacement relative to the gripper when object is slipping [19].…”

Section: The Slippage Signal Detection In Robotic Systemsmentioning

confidence: 99%

Section: The Slippage Signal Detection In Robotic Systemsmentioning

confidence: 99%

Section: Conductive Rubber Elements In Robotic Measurement Systemsmentioning

confidence: 99%

See 2 more Smart Citations

A Simulation Model for Robot’s Slip Displacement Sensors

Kondratenko¹,

Gerasin²,

Topalov³

2016

IJC

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“…It is well recognised that the acquisition of high-quality force and tactile sensor signals during a physical interaction between, say, a robot hand and an object being manipulated provides the opportunity to greatly improve the handling and manipulation capability of the robot system. Incorporating small-sized force/torque sensors in robot hands and arms has been shown to enhance obstacle avoidance, object grasping, in-hand manipulation and, generally, the meaningful interaction with the physical environment [1,2,3]. Current force measuring sensors show clear shortcomings due to their size, manufacturability, integration incompatibility, sensitivity, measurement range and/or lack of sufficient axes of measurement [4,5,6,7], with direct force measurements clearly outperforming approaches that use remote sensors to indirectly measure forces imparted on the robot structure.…”

Section: Introductionmentioning

confidence: 99%

Multi-Axis Force/Torque Sensor Based on Simply-Supported Beam and Optoelectronics

Noh

Bimbo

Sareh

et al. 2016

Sensors

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This paper presents a multi-axis force/torque sensor based on simply-supported beam and optoelectronic technology. The sensor’s main advantages are: (1) Low power consumption; (2) low-level noise in comparison with conventional methods of force sensing (e.g., using strain gauges); (3) the ability to be embedded into different mechanical structures; (4) miniaturisation; (5) simple manufacture and customisation to fit a wide-range of robot systems; and (6) low-cost fabrication and assembly of sensor structure. For these reasons, the proposed multi-axis force/torque sensor can be used in a wide range of application areas including medical robotics, manufacturing, and areas involving human–robot interaction. This paper shows the application of our concept of a force/torque sensor to flexible continuum manipulators: A cylindrical MIS (Minimally Invasive Surgery) robot, and includes its design, fabrication, and evaluation tests.

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Object Recognition for Humanoid Robots Using Full Hand Tactile Sensor

Pohtongkam

Srinonchat

2023

IEEE Access

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With the development of human-like robotic technologies, robots have been advanced to sense and recognize objects, which is one branch of artificial intelligence to mimic human reactions. Although high-performance computer technologies are used, the ability of object recognition by touch is still low due to the lack of proper sensors. Therefore, this research studied the object learning and recognition system through robot touches by developing an artificial sensory system acting as an electronic skin with tactile sensors. The Tactile Sensor is developed in this research, consisting of 15 Tactile Sensor Arrays and the palm's touchpoints. Furthermore, recognition analysis was developed on Bag of Word (BoW) and Convolution Neural Network (CNN) algorithms. With the BoW technique, using Support Vector Machine (SVM) as a classifier with Moment Analysis Descriptor (MA) provided the highest accuracy, showing more than 80.15% accuracy from five grasping of an object. With the CNN approach, InceptionNetV3 provided the highest accuracy of 98.28% from only one capture of an object.INDEX TERMS Bag of feature, bag of word, CNN, robot hand, tactile object recognition, tactile sensor, transfer learning.

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Design of a Tactile Sensor for Robot Hands

Cited by 10 publications

References 27 publications

A Simulation Model for Robot’s Slip Displacement Sensors

A Simulation Model for Robot’s Slip Displacement Sensors

Multi-Axis Force/Torque Sensor Based on Simply-Supported Beam and Optoelectronics

Object Recognition for Humanoid Robots Using Full Hand Tactile Sensor

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