A triangle histogram for object classification by tactile sensing

2016 ◽  
Author(s):  
Mabel M. Zhang ◽  
Monroe D. Kennedy ◽  
M. Ani Hsieh ◽  
Kostas Daniilidis
Keyword(s):  
Object Classification ◽  
Tactile Sensing

Object Classification and Grasp Planning Using Visual and Tactile Sensing

2016 ◽  
Vol 46 (7) ◽  
pp. 969-979 ◽  
Author(s):  
Fuchun Sun ◽  
Chunfang Liu ◽  
Wenbing Huang ◽  
Jianwei Zhang
Keyword(s):  
Object Classification ◽  
Tactile Sensing ◽  
Grasp Planning

scholarly journals Online Morphological Adaptation for Tactile Sensing Augmentation

2021 ◽  
Vol 8 ◽  
Author(s):  
Josie Hughes ◽  
Luca Scimeca ◽  
Perla Maiolino ◽  
Fumiya Iida
Keyword(s):  
Classification Accuracy ◽  
Object Classification ◽  
Tactile Sensor ◽  
Tactile Sensing ◽  
Morphological Adaptation ◽  
Sensor Structure ◽  
Morphology And Structure ◽  
Morphological Computation ◽  
Selection Of

Sensor morphology and structure has the ability to significantly aid and improve tactile sensing capabilities, through mechanisms such as improved sensitivity or morphological computation. However, different tactile tasks require different morphologies posing a challenge as to how to best design sensors, and also how to enable sensor morphology to be varied. We introduce a jamming filter which, when placed over a tactile sensor, allows the filter to be shaped and molded online, thus varying the sensor structure. We demonstrate how this is beneficial for sensory tasks analyzing how the change in sensor structure varies the information that is gained using the sensor. Moreover, we show that appropriate morphology can significantly influence discrimination, and observe how the selection of an appropriate filter can increase the object classification accuracy when using standard classifiers by up to 28%.

Fabrication and Characterization of Microcantilever for Detection of Vibration Sensation in Tactile Sensing

2019 ◽  
Vol 139 (11) ◽  
pp. 375-380
Author(s):  
Harutoshi Takahashi ◽  
Yuta Namba ◽  
Takashi Abe ◽  
Masayuki Sohgawa
Keyword(s):  
Tactile Sensing ◽  
Fabrication And Characterization

Using Dempster-Shafer Methods for Object Classification in the Theater Ballistic Missile Environment

1999 ◽  
Author(s):  
Kimberly Coombs ◽  
Debra Freel ◽  
Douglas Lampert ◽  
Steven Brahm
Keyword(s):  
Object Classification ◽  
Ballistic Missile

scholarly journals Towards Autonomous Robotic Assembly: Using Combined Visual and Tactile Sensing for Adaptive Task Execution

2021 ◽  
Vol 101 (3) ◽  
Author(s):  
Korbinian Nottensteiner ◽  
Arne Sachtler ◽  
Alin Albu-Schäffer
Keyword(s):  
Autonomous Robots ◽  
Vision System ◽  
Dynamic Environments ◽  
Bayesian Framework ◽  
Experimental Results ◽  
Robotic Assembly ◽  
Tactile Sensing ◽  
Task Execution ◽  
Industrial Assembly ◽  
Assembly Tasks

AbstractRobotic assembly tasks are typically implemented in static settings in which parts are kept at fixed locations by making use of part holders. Very few works deal with the problem of moving parts in industrial assembly applications. However, having autonomous robots that are able to execute assembly tasks in dynamic environments could lead to more flexible facilities with reduced implementation efforts for individual products. In this paper, we present a general approach towards autonomous robotic assembly that combines visual and intrinsic tactile sensing to continuously track parts within a single Bayesian framework. Based on this, it is possible to implement object-centric assembly skills that are guided by the estimated poses of the parts, including cases where occlusions block the vision system. In particular, we investigate the application of this approach for peg-in-hole assembly. A tilt-and-align strategy is implemented using a Cartesian impedance controller, and combined with an adaptive path executor. Experimental results with multiple part combinations are provided and analyzed in detail.

scholarly journals Real-time 2D–3D door detection and state classification on a low-power device

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

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