A new slip-detection method based on pairwise high frequency components of capacitive sensor signals

Yang, Haolin; Hu, Xiaohui; Cao, Lele; Sun, Fuchun

doi:10.1109/icist.2015.7288940

Cited by 12 publications

(8 citation statements)

References 15 publications

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“…In both works, when the high frequency signal produced by the tested sensors was greater than a threshold, slip could be revealed (even in its initial phase [90]). A slightly different approach was proposed in [92] where the DWT was computed on the output of a capacitive tactile sensor. A system comprising a dynamometer and a clamp exerted the force onto the sensor and slid several objects above the sensor surface.…”

Section: B Transform Techniquesmentioning

confidence: 99%

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Methods and Sensors for Slip Detection in Robotics: A Survey

Romeo

Zollo

2020

IEEE Access

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The perception of slip is one of the distinctive abilities of human tactile sensing. The sense of touch allows recognizing a wide set of properties of a grasped object, such as shape, weight and dimension. Based on such properties, the applied force can be accordingly regulated avoiding slip of the grasped object. Despite the great importance of tactile sensing for humans, mechatronic hands (robotic manipulators, prosthetic hands etc.) are rarely endowed with tactile feedback. The necessity to grasp objects relying on robust slip prevention algorithms is not yet corresponded in existing artificial manipulators, which are relegated to structured environments then. Numerous approaches regarding the problem of slip detection and correction have been developed especially in the last decade, resorting to a number of sensor typologies. However, no impact on the industrial market has been achieved. This paper reviews the sensors and methods so far proposed for slip prevention in artificial tactile perception, starting from more classical techniques until the latest solutions tested on robotic systems. The strengths and weaknesses of each described technique are discussed, also in relation to the sensing technologies employed. The result is a summary exploring the whole state of art and providing a perspective towards the future research directions in the sector.

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Section: B Transform Techniquesmentioning

confidence: 99%

“…Pairwise high frequency components from a trial (readapted from[92]). When a load is applied onto the object, variation from negative to positive value occurs.…”

mentioning

confidence: 99%

Methods and Sensors for Slip Detection in Robotics: A Survey

Romeo

Zollo

2020

IEEE Access

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“…In [54,55], the PDMS dielectric layer is improved by the micro needle structure, therefore inheriting and synthesizing both the advantages of the capacitive sensors with air gap and that with PDMS layers. The HCs-10 dataset [14,33] was gathered from this new type of capacitive tactile sensor array, several prototypes of which were recently made by Zhang et al [55]. The single cell size in the fabricated 4 × 4 sensors array is 0.7 × 0.5 cm 2 .…”

Section: Benchmark Datasetsmentioning

confidence: 99%

“…[11][12][13]), slippage detection (e.g. [14,15]), etc. In this paper, we address the problem of tactile object recognition, which aims at identifying target objects based on tactile data.…”

Section: Introductionmentioning

confidence: 99%

End-to-End ConvNet for Tactile Recognition Using Residual Orthogonal Tiling and Pyramid Convolution Ensemble

et al. 2018

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Background and Introduction Tactile recognition enables robots identify target objects or environments from tactile sensory readings. The recent advancement of deep learning and biological tactile sensing inspire us proposing an end-to-end architecture ROTConvPCE-mv that performs tactile recognition using residual orthogonal tiling and pyramid convolution ensemble. Methods Our approach uses stacks of raw frames and tactile flow as dual input, and incorporates the strength of multi-layer OTConvs (orthogonal tiling convolutions) organized in a residual learning paradigm. We empirically demonstrate that OTConvs have adjustable invariance capability to different input transformations such as translation, rotation, and scaling. To effectively capture multi-scale global context, a pyramid convolution structure is attached to the concatenated output of two residual OTConv pathways. Results and ConclusionsThe extensive experimental evaluations show that ROTConvPCE-mv outperforms several state-of-the-art methods with a large margin

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“…In this section, we evaluate 3T-RTCN on several benchmark datasets: SD5, SD10 (Bekiroglu, Kragic, and Kyrki 2010;Bekiroglu et al 2011), SPr7, SPr10 (Drimus et al 2014), BDH5 (Yang et al 2015b), BDH10 (Xiao et al 2014), and HCs10 (Yang et al 2015a), which represent different tactile recognition tasks with various complexities. SD5, SPr7, and BDH5 turn out to be similar to SD10, SPr10, and BDH10 respectively in many ways, so here we only focus on 4 datasets specified in Table 1.…”

Section: Experiments With Real-world Tasksmentioning

confidence: 99%

Efficient Spatio-Temporal Tactile Object Recognition with Randomized Tiling Convolutional Networks in a Hierarchical Fusion Strategy

Cao

Ramamohanarao

Sun

et al. 2016

AAAI

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Robotic tactile recognition aims at identifying target objects or environments from tactile sensory readings. The advancement of unsupervised feature learning and biological tactile sensing inspire us proposing the model of 3T-RTCN that performs spatio-temporal feature representation and fusion for tactile recognition. It decomposes tactile data into spatial and temporal threads, and incorporates the strength of randomized tiling convolutional networks. Experimental evaluations show that it outperforms some state-of-the-art methods with a large margin regarding recognition accuracy, robustness, and fault-tolerance; we also achieve an order-of-magnitude speedup over equivalent networks with pretraining and finetuning. Practical suggestions and hints are summarized in the end for effectively handling the tactile data.

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A new slip-detection method based on pairwise high frequency components of capacitive sensor signals

Cited by 12 publications

References 15 publications

Methods and Sensors for Slip Detection in Robotics: A Survey

Methods and Sensors for Slip Detection in Robotics: A Survey

End-to-End ConvNet for Tactile Recognition Using Residual Orthogonal Tiling and Pyramid Convolution Ensemble

Efficient Spatio-Temporal Tactile Object Recognition with Randomized Tiling Convolutional Networks in a Hierarchical Fusion Strategy

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