scholarly journals Combination of 2D and 3D vision systems into robotic cells for improved flexibility and performance

2011 ◽  
Author(s):  
Giovanna Sansoni ◽  
Paolo Bellandi ◽  
Franco Docchio
Keyword(s):  
Vision Systems ◽  
3D Vision ◽  
Robotic Cells ◽  
And Performance ◽  
2D And 3D

scholarly journals A New Method for Characterizing Bark Microrelief Using 3D Vision Systems

Forests ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 30 ◽  
Author(s):  
Andrzej Sioma ◽  
Jarosław Socha ◽  
Anna Klamerus-Iwan
Keyword(s):  
New Method ◽  
Vision Systems ◽  
3D Vision

High Yield Assembly of Compliant MEMS Snap Fasteners

2008 ◽  
Author(s):  
Rakesh Murthy ◽  
Aditya N. Das ◽  
Dan O. Popa
Keyword(s):  
Self Assembly ◽  
Building Blocks ◽  
Deformation Energy ◽  
High Yield ◽  
3 Dimensional ◽  
Lab Experiments ◽  
Trade Offs ◽  
Robotic Cells ◽  
And Performance ◽  
Important Design

Heterogeneous assembly at the microscale has recently emerged as a viable pathway to constructing 3-dimensional microrobots and other miniaturized devices. In contrast to self-assembly, this method is directed and deterministic, and is based on serial or parallel microassembly. Whereas at the meso and macro scales, automation is often undertaken after, and often benchmarked against manual assembly, we demonstrate that deterministic automation at the MEMS scale can be completed with higher yields through the use of engineered compliance and precision robotic cells. Snap fasteners have long been used as a way to exploit the inherent stability of local minima of the deformation energy caused by interference during part mating. In this paper we assume that the building blocks are 2 1/2 -dimensional, as is the case with lithographically microfabricated MEMS parts. The assembly of the snap fasteners is done using μ3, a multi-robot microassembly station with unique characteristics located at our ARRI’s Texas Microfactory lab. Experiments are performed to demonstrate that fast and reliable assemblies can be expected if the microparts and the robotic cell satisfy a so-called “High Yield Assembly Condition” (H.Y.A.C.). Important design trade-offs for assembly and performance of microsnap fasteners are discussed and experimentally evaluated.

scholarly journals Comparison of 2D and 3D Vision Gaze with Simultaneous Measurements of Accommodation and Convergence

Forma ◽  
2014 ◽  
Author(s):  
Hiroki Hori ◽  
Tomoki Shiomi ◽  
Satoshi Hasegawa ◽  
Hiroki Takada ◽  
Masako Omori ◽  
...  
Keyword(s):  
3D Vision ◽  
Simultaneous Measurements ◽  
2D And 3D

scholarly journals Detection of 2D and 3D Video Transitions Based on EEG Power

2020 ◽  
Author(s):  
Negin Manshouri ◽  
Mesut Melek ◽  
Temel Kayikcioglu
Keyword(s):  
Steady State ◽  
Support Vector ◽  
Success Rates ◽  
K Nearest Neighbors ◽  
3D Vision ◽  
Linear Discriminant ◽  
Parietal Lobes ◽  
Brain Signals ◽  
Sudden Transition ◽  
2D And 3D

Despite the long and extensive history of 3D technology, it has recently attracted the attention of researchers. This technology has become the center of interest of young people because of the real feelings and sensations it creates. People see their environment as 3D because of their eye structure. In this study, it is hypothesized that people lose their perception of depth during sleepy moments and that there is a sudden transition from 3D vision to 2D vision. Regarding these transitions, the EEG signal analysis method was used for deep and comprehensive analysis of 2D and 3D brain signals. In this study, a single-stream anaglyph video of random 2D and 3D segments was prepared. After watching this single video, the obtained EEG recordings were considered for two different analyses: the part involving the critical transition (transition-state) and the state analysis of only the 2D versus 3D or 3D versus 2D parts (steady-state). The main objective of this study is to see the behavioral changes of brain signals in 2D and 3D transitions. To clarify the impacts of the human brain’s power spectral density (PSD) in 2D-to-3D (2D_3D) and 3D-to-2D (3D_2D) transitions of anaglyph video, 9 visual healthy individuals were prepared for testing in this pioneering study. Spectrogram graphs based on Short Time Fourier transform (STFT) were considered to evaluate the power spectrum analysis in each EEG channel of transition or steady-state. Thus, in 2D and 3D transition scenarios, important channels representing EEG frequency bands and brain lobes will be identified. To classify the 2D and 3D transitions, the dominant bands and time intervals representing the maximum difference of PSD were selected. Afterward, effective features were selected by applying statistical methods such as standard deviation (SD), maximum (max), and Hjorth parameters to epochs indicating transition intervals. Ultimately, k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The frontal, temporal, and partially parietal lobes show 2D_3D and 3D_2D transitions with a good classification success rate. Overall, it was found that Hjorth parameters and LDA algorithms have 71.11% and 77.78% classification success rates for transition and steady-state, respectively.

A 3D Vision-Based Solution for Product Picking In Industrial Applications

2010 ◽  
pp. 190-208
Author(s):  
Mirko Sgarbi ◽  
Valentina Colla ◽  
Gianluca Bioli
Keyword(s):  
Robot Manipulator ◽  
Vision System ◽  
Conveyor Belt ◽  
Industrial Applications ◽  
Vision Systems ◽  
3D Vision ◽  
Key Factor ◽  
Control Assembly ◽  
Single Marker ◽  
Bin Picking

Computer vision is nowadays a key factor in many manufacturing processes. Among all possible applications like quality control, assembly verification and component tracking, the robot guidance for pick and place operations can assume an important role in increasing the automation level of production lines. While 3D vision systems are now emerging as valid solutions in bin-picking applications, where objects are randomly placed inside a box, 2D vision systems are widely and successfully adopted when objects are placed on a conveyor belt and the robot manipulator can grasp the object by exploiting only the 2D information. On the other hand, there are many real-world applications where the 3rd dimension is required by the picking system. For example, the objects can differ in their height or they can be manually placed in front of the camera without any constraint on the distance between the object and the camera itself. Although a 3D vision system could represent a possible solution, 3D systems are more complex, more expensive and less compact than 2D vision systems. This chapter describes a monocular system useful for picking applications. It can estimate the 3D position of a single marker attached to the target object assuming that the orientation of the object is approximately known.

scholarly journals Fusing Data Processing in the Construction of Machine Vision Systems in Robotic Complexes

2019 ◽  
Vol 224 ◽  
pp. 04009 ◽  
Author(s):  
Aleksandr Zelensky ◽  
Evgenii Semenishchev ◽  
Aleksandr Gavlicky ◽  
Irina Tolstova ◽  
V. Frantc
Keyword(s):  
Machine Vision ◽  
Electromagnetic Interference ◽  
Visual Information ◽  
Vision Systems ◽  
Noise Component ◽  
New Approach ◽  
Common Causes ◽  
Robotic Cells ◽  
Combined Images

The development of machine vision systems is based on the analysis of visual information recorded by sensitive matrices. This information is most often distorted by the presence of interfering factors represented by a noise component. The common causes of the noise include imperfect sensors, dust and aerosols, used ADCs, electromagnetic interference, and others. The presence of these noise components reduces the quality of the subsequent analysis. To implement systems that allow operating in the presence of a noise, a new approach, which allows parallel processing of data obtained in various electromagnetic ranges, has been proposed. The primary area of application of the approach are machine vision systems used in complex robotic cells. The use of additional data obtained by a group of sensors allows the formation of arrays of usefull information that provide successfull optimization of operations. The set of test data shows the applicability of the proposed approach to combined images in machine vision systems.

Detection of 2D and 3D Video Transitions Based on EEG Power

2020 ◽  
Author(s):  
Negin Manshouri ◽  
Mesut Melek ◽  
Temel Kayıkcıoglu
Keyword(s):  
Steady State ◽  
Support Vector ◽  
Success Rates ◽  
K Nearest Neighbors ◽  
3D Vision ◽  
Linear Discriminant ◽  
Parietal Lobes ◽  
Brain Signals ◽  
Sudden Transition ◽  
2D And 3D

Abstract Despite the long and extensive history of 3D technology, it has recently attracted the attention of researchers. This technology has become the center of interest of young people because of the real feelings and sensations it creates. People see their environment as 3D because of their eye structure. In this study, it is hypothesized that people lose their perception of depth during sleepy moments and that there is a sudden transition from 3D vision to 2D vision. Regarding these transitions, the EEG signal analysis method was used for deep and comprehensive analysis of 2D and 3D brain signals. In this study, a single-stream anaglyph video of random 2D and 3D segments was prepared. After watching this single video, the obtained EEG recordings were considered for two different analyses: the part involving the critical transition (transition state) and the state analysis of only the 2D versus 3D or 3D versus 2D parts (steady state). The main objective of this study is to see the behavioral changes of brain signals in 2D and 3D transitions. To clarify the impacts of the human brain’s power spectral density (PSD) in 2D-to-3D (2D_3D) and 3D-to-2D (3D_2D) transitions of anaglyph video, nine visual healthy individuals were prepared for testing in this pioneering study. Spectrogram graphs based on short time Fourier transform (STFT) were considered to evaluate the power spectrum analysis in each EEG channel of transition or steady state. Thus, in 2D and 3D transition scenarios, important channels representing EEG frequency bands and brain lobes will be identified. To classify the 2D and 3D transitions, the dominant bands and time intervals representing the maximum difference of PSD were selected. Afterward, effective features were selected by applying statistical methods such as standard deviation, maximum (max) and Hjorth parameters to epochs indicating transition intervals. Ultimately, k-nearest neighbors, support vector machine and linear discriminant analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The frontal, temporal and partially parietal lobes show 2D_3D and 3D_2D transitions with a good classification success rate. Overall, it was found that Hjorth parameters and LDA algorithms have 71.11% and 77.78% classification success rates for transition and steady state, respectively.

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