Perception modeling for human-like artificial sensor systems

Robertsson, Linn; Iliev, Boyko; Palm, Rainer; Wide, Peter

doi:10.1016/j.ijhcs.2006.11.003

Cited by 27 publications

(13 citation statements)

References 13 publications

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“…A tactile sensor array can be developed with several operating principles, and it has various shapes and sizes depending on applications [7][8][9][10][11][14][15][16][17][18][19][20]. There are many types of tactile sensors, which can be categorized by the working principles such as piezoresistive [21,22], capacitive [23,24], piezoelectric [25,26], and optical [27,28], etc.…”

Section: Tactile Sensor Arraymentioning

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: Tactile Sensor Arraymentioning

confidence: 99%

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

Pohtongkam

Srinonchat

2021

Sensors

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“…Bruce et al address a planned human-robot rendezvous at an intersection zone [7]. Human-like sensors/systems allow for easier and more natural human-robot interaction because they share their principle of operation with natural systems [8], [9], [10]. Based on an estimation of positions and orientations of robot and human, the intersections of intended linear trajectories of robot and human are computed.…”

Section: Introductionmentioning

confidence: 99%

“…Output distribution: To obtain the distribution 1 , 2 of the output signal we invert(11) and substitute the entries of e x into(8) e x = ⋅ e z…”

mentioning

confidence: 99%

Gaussian noise and the intersection problem in Human-Robot Systems - analytical and fuzzy approach

Palm¹,

Lilienthal²

2019

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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In this paper the intersection problem in humanrobot systems with respect to noisy information is discussed. The interaction between humans and mobile robots in shared areas requires a high level of safety especially at the intersections of trajectories. We discuss the intersection problem with respect to noisy information on the basis of an analytic geometrical model and its TS fuzzy version. The transmission of a 2-dimensional Gaussian noise signal, in particular information on human and robot orientations, through a non-linear static system and its fuzzy version, will be described. We discuss the problem: Given the parameters of the input distributions, find the parameters of the output distributions.

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“…However, due to the many inherent weaknesses of panel tests, researchers are proposing multi-modality sensing to assist human panels in making their decisions. The aim of this concept is to partially emulate the human sensory systems ( i.e ., smell and taste) electronically and combine them in a similar manner to how they work in the human brain [ 2 , 3 ]. This multi-modality sensor fusion has been accepted in a wide range of specific applications such as the military, medicine and agriculture [ 4 – 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Biomimetic Sensor for the Classification of Honeys of Different Floral Origin and the Detection of Adulteration

Zakaria

Shakaff

Masnan

et al. 2011

Sensors

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The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.

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Perception modeling for human-like artificial sensor systems

Cited by 27 publications

References 13 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

Gaussian noise and the intersection problem in Human-Robot Systems - analytical and fuzzy approach

A Biomimetic Sensor for the Classification of Honeys of Different Floral Origin and the Detection of Adulteration

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