High-resolution structured light 3D vision for fine-scale characterization to assist robotic assembly

2021 ◽  
Author(s):  
Vignesh Suresh ◽  
Wansong Liu ◽  
Minghui Zheng ◽  
Beiwen Li
Keyword(s):  
High Resolution ◽  
Structured Light ◽  
Robotic Assembly ◽  
Fine Scale ◽  
3D Vision ◽  
Scale Characterization

High Resolution Stereography of Complementary Freeze-Fracture Replicas Reveals the Presence of Depressions Opposite Membrane Associated Particles of Split Membranes

1971 ◽  
Vol 29 ◽  
pp. 442-443
Author(s):  
Russell L. Steere
Keyword(s):  
High Resolution ◽  
Cardiac Muscle ◽  
Electron Micrographs ◽  
Chromic Acid ◽  
Freeze Fracture ◽  
Distilled Water ◽  
Fine Scale ◽  
Acid Cleaning ◽  
Cleaning Solution

Complementary replicas have revealed the fact that the two common faces observed in electron micrographs of freeze-fracture and freeze-etch specimens are complementary to each other and are thus the new faces of a split membrane rather than the original inner and outer surfaces (1, 2 and personal observations). The big question raised by published electron micrographs is why do we not see depressions in the complementary face opposite membrane-associated particles? Reports have appeared indicating that some depressions do appear but complementarity on such a fine scale has yet to be shown.Dog cardiac muscle was perfused with glutaraldehyde, washed in distilled water, then transferred to 30% glycerol (material furnished by Dr. Joaquim Sommer, Duke Univ., and VA Hospital, Durham, N.C.). Small strips were freeze-fractured in a Denton Vacuum DFE-2 Freeze-Etch Unit with complementary replica tooling. Replicas were cleaned in chromic acid cleaning solution, then washed in 4 changes of distilled water and mounted on opposite sides of the center wire of a Formvar-coated grid.

Human skill integrated motion planning of assembly manipulation for 6R industrial robot

2019 ◽  
Vol 46 (1) ◽  
pp. 171-180
Author(s):  
Yi Liu ◽  
Ming Cong ◽  
Hang Dong ◽  
Dong Liu
Keyword(s):  
Deep Learning ◽  
Motion Planning ◽  
Industrial Robot ◽  
Robotic Assembly ◽  
3D Vision ◽  
Content Type ◽  
Human Skill ◽  
Depth Images ◽  
Positioning Method ◽  
Assembly Technology

Purpose The purpose of this paper is to propose a new method based on three-dimensional (3D) vision technologies and human skill integrated deep learning to solve assembly positioning task such as peg-in-hole. Design/methodology/approach Hybrid camera configuration was used to provide the global and local views. Eye-in-hand mode guided the peg to be in contact with the hole plate using 3D vision in global view. When the peg was in contact with the workpiece surface, eye-to-hand mode provided the local view to accomplish peg-hole positioning based on trained CNN. Findings The results of assembly positioning experiments proved that the proposed method successfully distinguished the target hole from the other same size holes according to the CNN. The robot planned the motion according to the depth images and human skill guide line. The final positioning precision was good enough for the robot to carry out force controlled assembly. Practical implications The developed framework can have an important impact on robotic assembly positioning process, which combine with the existing force-guidance assembly technology as to build a whole set of autonomous assembly technology. Originality/value This paper proposed a new approach to the robotic assembly positioning based on 3D visual technologies and human skill integrated deep learning. Dual cameras swapping mode was used to provide visual feedback for the entire assembly motion planning process. The proposed workpiece positioning method provided an effective disturbance rejection, autonomous motion planning and increased overall performance with depth images feedback. The proposed peg-hole positioning method with human skill integrated provided the capability of target perceptual aliasing avoiding and successive motion decision for the robotic assembly manipulation.

Very high resolution environmental predictors in species distribution models

2013 ◽  
Vol 38 (1) ◽  
pp. 79-96 ◽  
Author(s):  
Jean-Nicolas Pradervand ◽  
Anne Dubuis ◽  
Loïc Pellissier ◽  
Antoine Guisan ◽  
Christophe Randin
Keyword(s):  
High Resolution ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Environmental Data ◽  
Swiss Alps ◽  
Fine Scale ◽  
Distribution Models ◽  
Model Quality ◽  
Vegetation Height ◽  
Very High

Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species’ micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions – and therefore local management – compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management.

scholarly journals A thirty year, fine-scale, characterization of area burned in Canadian forests shows evidence of regionally increasing trends in the last decade

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0197218 ◽  
Author(s):  
Nicholas C. Coops ◽  
Txomin Hermosilla ◽  
Michael A. Wulder ◽  
Joanne C. White ◽  
Douglas K. Bolton
Keyword(s):  
Fine Scale ◽  
Area Burned ◽  
Scale Characterization ◽  
Increasing Trends

Real-Time Sensing of Sag Geometry During GTA Welding

1997 ◽  
Vol 119 (2) ◽  
pp. 151-160 ◽  
Author(s):  
Y. M. Zhang ◽  
R. Kovacevic
Keyword(s):  
Real Time ◽  
Vision System ◽  
Structured Light ◽  
Gta Welding ◽  
Seam Tracking ◽  
Image Processing Algorithm ◽  
3D Vision ◽  
Full Penetration ◽  
Laser Stripe ◽  
Penetration Control

Seam tracking and weld penetration control are two fundamental issues in automated welding. Although the seam tracking technique has matured, the latter still remains a unique unsolved problem. It was found that the full penetration status during GTA welding can be determined with sufficient accuracy using the sag depression. To achieve a new full penetration sensing technique, a structured-light 3D vision system is developed to extract the sag geometry behind the pool. The laser stripe, which is the intersection of the structured-light and weldment, is thinned and then used to acquire the sag geometry. To reduce possible control delay, a small distance is selected between the pool rear and laser stripe. An adaptive dynamic search for rapid thinning of the stripe and the maximum principle of slope difference for unbiased recognition of sag border were proposed to develop an effective real-time image processing algorithm for sag geometry acquisition. Experiments have shown that the proposed sensor and image algorithm can provide reliable feedback information of sag geometry for the full penetration control system.

scholarly journals Augmenting biologging with supervised machine learning to studyin situbehavior of the medusaChrysaora fuscescens

2019 ◽  
Author(s):  
Clara Fannjiang ◽  
T. Aran Mooney ◽  
Seth Cones ◽  
David Mann ◽  
K. Alex Shorter ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Laboratory Data ◽  
Supervised Machine Learning ◽  
Motion Sensors ◽  
Fine Scale ◽  
Video Footage ◽  
Research Findings ◽  
Summary Statement

AbstractZooplankton occupy critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood due to the difficulty of studying individualsin situ. Here we combine biologging with supervised machine learning (ML) to demonstrate a pipeline for studyingin situbehavior of larger zooplankton such as jellyfish. We deployed the ITAG, a biologging package with high-resolution motion sensors designed for soft-bodied invertebrates, on 8Chrysaora fuscescensin Monterey Bay, using the tether method for retrieval. Using simultaneous video footage of the tagged jellyfish, we develop ML methods to 1) identify periods of tag data corrupted by the tether method, which may have compromised prior research findings, and 2) classify jellyfish behaviors. Our tools yield characterizations of fine-scale jellyfish activity and orientation over long durations, and provide evidence that developing behavioral classifiers onin siturather than laboratory data is essential.Summary StatementHigh-resolution motion sensors paired with supervised machine learning can be used to infer fine-scalein situbehavior of zooplankton for long durations.

scholarly journals All Our Eggs In One Basket: Challenges of High Resolution X-Ray Micro-Computed Tomography of Great Auk Pinguinus impennis Eggshell

2018 ◽  
Vol 2 ◽  
pp. e25794
Author(s):  
Douglas Russell ◽  
Arianna Bernucci ◽  
Amy Scott-Murray ◽  
Duncan Jackson ◽  
Farah Ahmed ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Structured Light ◽  
3D Models ◽  
Breeding Ecology ◽  
Micro Computed Tomography ◽  
History Museum ◽  
X Ray ◽  
X Ray Imaging ◽  
Scan Data

High resolution X-ray micro-computed tomography gives the ability to research objects in unprecedented detail in 3D without damaging them but applying these new techniques to specimens can be complex. In 2017 the Natural History Museum (NHM), London embarked on a ground-breaking project with University of Sheffield to compare extinct Great Auk Pinguinus impennis eggshell microstructure to that of their extant relatives to gain new insight into their breeding ecology. NHM has a ZEISS Xradia 520 Versa X-ray microscope capable of submicron X-ray imaging in 3D but using it required supporting and moving complete eggshells within the confined, potentially harsh, mechanised environment of the microscope without risk. Ensuring the correct position and orientation of each egg to image nine distinct areas on the eggshell was also a challenge. Collaboration with colleagues in the NHM Conservation and Imaging & Analysis Centres developed a bespoke solution to hold and protect the eggs during scanning. All six NHM Great Auk eggshells and the inside of the microscope were surface scanned using a handheld structured light scanner. Scan data produced 3D models from which accurate 3D printed plastic replicas were made of the three Great Auk eggs prioritised for research. Each replica was used to mould a two-part, custom-built, case for each egg constructed from conservation grade epoxy putty and lined with polyethylene foam. This provided close-fitting, durable cases which could be used for the 6-month duration of the project. Each case enclosed its matching Great Auk egg entirely and had the advantage of being rock-hard, electrically insulating and water, heat and chemical resistant. A system of three, interchangeable, tailor-made mounting brackets were designed that married with the cases and held them safely and precisely inside the microscope at the correct angles and positions for imaging. The structured light scan of the inside of the microscope was used to model the necessary rotational movements of the cases and brackets inside the scanner, ensuring that all movements had sufficient clearance to avoid risk of impact. This system successfully protected the fragile c. 200 year old eggs throughout 70 scanning sessions. This provides a methodology for high resolution X-ray micro-computed tomography imaging of any similarly sized, fragile, object.

scholarly journals Swarm v2: highly-scalable and high-resolution amplicon clustering

2015 ◽  
Author(s):  
Frédéric Mahé ◽  
Torbjørn Rognes ◽  
Christopher Quince ◽  
Colomban de Vargas ◽  
Micah S Dunthorn
Keyword(s):  
High Resolution ◽  
Initial Phase ◽  
Computation Time ◽  
Two Dimensional ◽  
Fine Scale ◽  
Single Linkage ◽  
Fasta Format ◽  
Operational Taxonomic Units ◽  
Abundant Otus ◽  
Local Clustering

Previously we presented Swarm v1, a novel and open source amplicon clustering program that produced fine-scale molecular operational taxonomic units (OTUs), free of arbitrary global clustering thresholds and input-order dependency. Swarm v1 worked with an initial phase that used iterative single-linkage with a local clustering threshold (d), followed by a phase that used the internal abundance structures of clusters to break chained OTUs. Here we present Swarm v2 that has two important novel features: 1) a new algorithm for d = 1 that allows the computation time of the program to scale linearly with increasing amounts of data; and 2) the new fastidious option that reduces under-grouping by grafting low abundant OTUs (e.g., singletons and doubletons) onto larger ones. Swarm v2 also directly integrates the clustering and breaking phases, dereplicates sequencing reads with d = 0, outputs OTU representatives in fasta format, and plots individual OTUs as two-dimensional networks.

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