The Use of Tactile Sensors and PIV Analysis for Understanding the Bearing Mechanism of Pile Groups

You, Zhijia; Chen, Yulong

doi:10.3390/s18020476

Cited by 6 publications

(8 citation statements)

References 9 publications

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“…This research focused on designing the tactile sensor array based on the piezoresistive principle due to the simple structure, high sensitivity, low cost, and robustness [ 9 , 10 , 16 , 20 , 21 ]. This type of tactile sensor array is commonly used in various applications such as medical [ 5 , 7 , 20 ], industrial manufacturing [ 29 ], civil engineering [ 30 ], and human-like activities [ 31 ], etc. Recently, the application of piezoresistive tactile sensors for humanoid robots has been gaining much interest [ 1 , 2 , 5 , 8 ] [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ].…”

Section: Related Workmentioning

confidence: 99%

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

Pohtongkam

Srinonchat

2021

Sensors

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A tactile sensor array is a crucial component for applying physical sensors to a humanoid robot. This work focused on developing a palm-size tactile sensor array (56.0 mm × 56.0 mm) to apply object recognition for the humanoid robot hand. This sensor was based on a PCB technology operating with the piezoresistive principle. A conductive polymer composites sheet was used as a sensing element and the matrix array of this sensor was 16 × 16 pixels. The sensitivity of this sensor was evaluated and the sensor was installed on the robot hand. The tactile images, with resolution enhancement using bicubic interpolation obtained from 20 classes, were used to train and test 19 different DCNNs. InceptionResNetV2 provided superior performance with 91.82% accuracy. However, using the multimodal learning method that included InceptionResNetV2 and XceptionNet, the highest recognition rate of 92.73% was achieved. Moreover, this recognition rate improved when the object exploration was applied to demonstrate.

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Section: Related Workmentioning

confidence: 99%

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

Pohtongkam

Srinonchat

2021

Sensors

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“…Furthermore, this algorithm can run very efficiently, which is desirable when processing large data sets from multiple measuring locations. Lastly, this alignment algorithm is applied to process the velocity data from ADVs, but it is worth noticing that this algorithm is also applicable for other velocity measuring technologies, e.g., EMF (electromagnetic flow manufacture meter) and PIV (particle image velocimetry) [41][42][43].…”

Section: Advantages Of the Current Measuring System And Alignment Algmentioning

confidence: 99%

Applying a New Force–Velocity Synchronizing Algorithm to Derive Drag Coefficients of Rigid Vegetation in Oscillatory Flows

Yao

Chen

Huang

et al. 2018

Water

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Coastal vegetation is effective in dissipating incident wave energy during storm conditions, which offers valuable protection to coastal communities. Determining vegetation drag coefficient (C D) is of great importance to the quantification of vegetation-induced wave dissipation. Recently, a direct measuring approach has been developed to derive vegetation drag coefficient more accurately compared to the conventional calibration approach. However, as this approach requires perfectly in-phase force and velocity signals, there are two difficulties associated with it. The first difficulty is the availability of a suitable force sensor to compose synchronized force-velocity measuring systems. The second difficulty is related to realigning the obtained timeseries of force and velocity data. This technical note develops a new synchronized force-velocity measuring system by using standard force sensors and an acoustic doppler velocimeter (ADV). This system is applied together with an automatic realignment algorithm to ensure in-phase data for C D deviation. The algorithm reduces the phase shift between force-velocity signals from ca. 0.26 s to 0.003 s. Both time-varying and period-averaged C D can be obtained using this method. The derived C D can be used to accurately reproduce the measured maximum total acting force on vegetation (R 2 = 0.759), which shows the reliability of the automatic alignment algorithm. The newly-developed synchronized force-velocity measuring system and alignment algorithm are expected to be useful in future experiments on vegetation-wave interactions with various hydrodynamic and vegetation settings.

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“…When piles in a group are placed with close space, the induced stresses and strains in the surrounding soil overlap, so their behavior becomes different from a single pile, which is working in the same conditions [1][2][3][4][5][6][7][8][9][10][11][12]. To understand the performance of a group pile, many model tests were conducted by previous researchers, such as T. Whitaker 1957 [11]; A,B.…”

Section: Introductionmentioning

confidence: 99%

“…2013 [1]; Goto S. et al 2013 [4]; Patil J.D. et al 2015 [9]; Zhijia You and Yulong Chen 2018 [6]; etc. Big-budget of making load tests on fullscale pile group at the site and the high variability of field conditions make them difficult for research aims.…”

Section: Introductionmentioning

confidence: 99%

Static loading tests on small-scale pile groups

Hoang¹

2021

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36 small-scale model tests in soft clay were conducted to research the performances of pile groups under rigid caps. The parameters studied were the effect of pile length, pile spacing, and the number of piles in a group. The group piles consisted of 4, 6, and 9 circular model piles of 16mm in outer diameter (D), while four kinds of the pile spacing between pile centers 3; 4; 5; and 6 times of the diameter and three types of the embedded pile lengths: 20D; 25D; and 30D were used. For comparison, three single piles with the same diameter and length were also tested under the same condition. The experimental results were discussed based on the following 3 points of view: the pile group efficiency, the settlement ratio, load distribution per pile location in the group pile. All discussion suggested that the pile number and pile spacing in a pile group caused a remarkable interactional effect between piles, whereas the settlement ratios are significantly affected by the pile length. Besides, each pile in the group of 6D pile spacing behaved more individually.

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The Use of Tactile Sensors and PIV Analysis for Understanding the Bearing Mechanism of Pile Groups

Cited by 6 publications

References 9 publications

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

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

Applying a New Force–Velocity Synchronizing Algorithm to Derive Drag Coefficients of Rigid Vegetation in Oscillatory Flows

Static loading tests on small-scale pile groups

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