Tactile Object Recognition for Humanoid Robots Using New Designed Piezoresistive Tactile Sensor and DCNN

Pohtongkam, Somchai; Srinonchat, Jakkree

doi:10.3390/s21186024

Cited by 28 publications

(25 citation statements)

References 76 publications

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“…Therefore, this research develops object recognition for humanoid robots using full-hand tactile sensors, which achieves to carry and continues from our previous work [6].…”

Section: Introductionmentioning

confidence: 90%

“…The related work in object recognition by handling is quite diverse due to developments in several aspects of this subject, beginning with studies of human haptic behavior [7] from physiological and psychological studies to understand human haptic perception and unfamiliar objects surveys for recognition [8]. Other studies include humanoid robot hand system design to enable object handling [3], [4], [9], [10], control of the robot hand system of a humanoid robot [11], [12], tactile sensor array design for humanoids robot hand [5], [6], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32]. Methods for recognizing objects based on tactile image recognition [6], [15], [18], [20], [21], [22], [23], [24], [28], [29], [31], [32], [33], [34], [35],…”

Section: Theory and Related Workmentioning

confidence: 99%

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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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“…Therefore, this research develops object recognition for humanoid robots using full-hand tactile sensors, which achieves to carry and continues from our previous work [6].…”

Section: Introductionmentioning

confidence: 90%

Section: Theory and Related Workmentioning

confidence: 99%

Object Recognition for Humanoid Robots Using Full Hand Tactile Sensor

Pohtongkam

Srinonchat

2023

IEEE Access

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“…Piezo technology has been used for piezoresistive and piezoelectric tactile sensors for robot fingers and palms to recognise deformable and rigid objects [13], to detect slippage [14] and as array of sensors in robot hands for object recognition based on the object size and stiffness [15]. Tactile sensors with piezo technology are relatively easy to build, low cost, and can provide high sensitivity and high frequency.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Barometric and Inertial Measurement Unit-Based Tactile Sensor for Robot Control

Martinez-Hernandez

2023

IEEE Sensors J.

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In this paper, we present a low-cost multimodal tactile sensor capable of providing accelerometer, gyroscope and pressure data using a 7-axis chip as sensing element. This approach reduces the complexity of tactile sensor design and collection of multimodal data. The tactile device is composed of a top layer (a printed circuit board and sensing element), a middle layer (soft rubber material), and bottom layer (plastic base) forming a sandwich structure. This approach allows the measurement of multimodal data when force is applied on different parts of the top layer of the sensor. The multimodal tactile sensor is validated with analyses and experiments in both offline and real-time. First, the spatial impulse response and sensitivity of the sensor are analysed with accelerometer, gyroscope and pressure data systematically collected from the sensor. Second, estimation of contact location from a range of sensor positions and force values is evaluated using accelerometer and gyroscope data together with a Convolutional Neural Network (CNN) method. Third, the estimation of contact location is used to control the position of a robot arm. The results show that the proposed multimodal tactile sensor has the potential for robotic applications such as tactile perception for robot control, human-robot interaction and object exploration.

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“…In this process, using the method of manually defining features may result in losing important feature information, which may lead to a deviation in the results. However, many studies [ 25 , 26 , 27 , 28 ] have shown that deep learning algorithms play an important part in the tactile recognition of intelligent robots. The tactile recognition method, based on deep learning algorithms, can extract useful features from input vectors and greatly reduce the computational complexity [ 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Hardness-and-Type Recognition of Different Objects Based on a Novel Porous Graphene Flexible Tactile Sensor Array

Yang

Wang

et al. 2023

Micromachines

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Accurately recognizing the hardness and type of different objects by tactile sensors is of great significance in human–machine interaction. In this paper, a novel porous graphene flexible tactile sensor array with great performance is designed and fabricated, and it is mounted on a two-finger mechanical actuator. This is used to detect various tactile sequence features from different objects by slightly squeezing them by 2 mm. A Residual Network (ResNet) model, with excellent adaptivity and feature extraction ability, is constructed to realize the recognition of 4 hardness categories and 12 object types, based on the tactile time sequence signals collected by the novel sensor array; the average accuracies of hardness and type recognition are 100% and 99.7%, respectively. To further verify the classification ability of the ResNet model for the tactile feature information detected by the sensor array, the Multilayer Perceptron (MLP), LeNet, Multi-Channel Deep Convolutional Neural Network (MCDCNN), and ENCODER models are built based on the same dataset used for the ResNet model. The average recognition accuracies of the 4hardness categories, based on those four models, are 93.6%, 98.3%, 93.3%, and 98.1%. Meanwhile, the average recognition accuracies of the 12 object types, based on the four models, are 94.7%, 98.9%, 85.0%, and 96.4%. All of the results demonstrate that the novel porous graphene tactile sensor array has excellent perceptual performance and the ResNet model can very effectively and precisely complete the hardness and type recognition of objects for the flexible tactile sensor array.

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Tactile Object Recognition for Humanoid Robots Using New Designed Piezoresistive Tactile Sensor and DCNN

Cited by 28 publications

References 76 publications

Object Recognition for Humanoid Robots Using Full Hand Tactile Sensor

Object Recognition for Humanoid Robots Using Full Hand Tactile Sensor

Multimodal Barometric and Inertial Measurement Unit-Based Tactile Sensor for Robot Control

Hardness-and-Type Recognition of Different Objects Based on a Novel Porous Graphene Flexible Tactile Sensor Array

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