skip to main content

Attention:

The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 11:00 PM ET on Friday, November 15 until 2:00 AM ET on Saturday, November 16 due to maintenance. We apologize for the inconvenience.


Title: Robotic painting: mimicking human applicators
Abstract

Robotically assisted painting is widely used for spray and dip applications. However, use of robots for coating substrates using a roller applicator has not been systematically investigated. We showed for the first time, a generic robot arm-supported approach to painting engineering substrates using a roller with a constant force at an accurate joint step, while retaining compliance and thus safety. We optimized the robot design such that it is able to coat the substrate using a roller with a performance equivalent to that of a human applicator. To achieve this, we optimized the force, frequency of adjustment, and position control parameters of robotic design. A framework for autonomous coating is available athttps://github.com/duyayun/Vision-and-force-control-automonous-painting-with-rollers; users are only required to provide the boundary coordinates of surfaces to be coated. We found that robotically- and human-painted panels showed similar trends in dry film thickness, coating hardness, flexibility, impact resistance, and microscopic properties. Color profile analysis of the coated panels showed non-significant difference in color scheme and is acceptable for architectural paints. Overall, this work shows the potential of robot-assisted coating strategy using roller applicator. This could be a viable option for hazardous area coating, high-altitude architectural paints, germs sanitization, and accelerated household applications.

 
more » « less
Award ID(s):
1925360
NSF-PAR ID:
10403168
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Springer Science + Business Media
Date Published:
Journal Name:
Journal of Coatings Technology and Research
Volume:
20
Issue:
4
ISSN:
1547-0091
Page Range / eLocation ID:
p. 1369-1381
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    We present a new method and software tool called that applies a pangenome index to the problem of inferring genotypes from short-read sequencing data. The method uses a novel indexing structure called the marker array. Using the marker array, we can genotype variants with respect from large panels like the 1000 Genomes Project while reducing the reference bias that results when aligning to a single linear reference. can infer accurate genotypes in less time and memory compared to existing graph-based methods. The method is implemented in the open source software tool available athttps://github.com/alshai/rowbowt.

     
    more » « less
  2. Abstract Summary

    dadi is a popular software package for inferring models of demographic history and natural selection from population genomic data. But using dadi requires Python scripting and manual parallelization of optimization jobs. We developed dadi-cli to simplify dadi usage and also enable straighforward distributed computing.

    Availability and Implementation

    dadi-cli is implemented in Python and released under the Apache License 2.0. The source code is available athttps://github.com/xin-huang/dadi-cli. dadi-cli can be installed via PyPI and conda, and is also available through Cacao on Jetstream2https://cacao.jetstream-cloud.org/.

     
    more » « less
  3. Abstract Background

    Plant architecture can influence crop yield and quality. Manual extraction of architectural traits is, however, time-consuming, tedious, and error prone. The trait estimation from 3D data addresses occlusion issues with the availability of depth information while deep learning approaches enable learning features without manual design. The goal of this study was to develop a data processing workflow by leveraging 3D deep learning models and a novel 3D data annotation tool to segment cotton plant parts and derive important architectural traits.

    Results

    The Point Voxel Convolutional Neural Network (PVCNN) combining both point- and voxel-based representations of 3D data shows less time consumption and better segmentation performance than point-based networks. Results indicate that the best mIoU (89.12%) and accuracy (96.19%) with average inference time of 0.88 s were achieved through PVCNN, compared to Pointnet and Pointnet++. On the seven derived architectural traits from segmented parts, an R2value of more than 0.8 and mean absolute percentage error of less than 10% were attained.

    Conclusion

    This plant part segmentation method based on 3D deep learning enables effective and efficient architectural trait measurement from point clouds, which could be useful to advance plant breeding programs and characterization of in-season developmental traits. The plant part segmentation code is available athttps://github.com/UGA-BSAIL/plant_3d_deep_learning.

     
    more » « less
  4. In this article, we propose TAPA, an end-to-end framework that compiles a C++ task-parallel dataflow program into a high-frequency FPGA accelerator. Compared to existing solutions, TAPA has two major advantages. First, TAPA provides a set of convenient APIs that allows users to easily express flexible and complex inter-task communication structures. Second, TAPA adopts a coarse-grained floorplanning step during HLS compilation for accurate pipelining of potential critical paths. In addition, TAPA implements several optimization techniques specifically tailored for modern HBM-based FPGAs. In our experiments with a total of 43 designs, we improve the average frequency from 147 MHz to 297 MHz (a 102% improvement) with no loss of throughput and a negligible change in resource utilization. Notably, in 16 experiments, we make the originally unroutable designs achieve 274 MHz, on average. The framework is available athttps://github.com/UCLA-VAST/tapaand the core floorplan module is available athttps://github.com/UCLA-VAST/AutoBridge

     
    more » « less
  5. Abstract Background

    The pan-genome of a species is the union of the genes and non-coding sequences present in all individuals (cultivar, accessions, or strains) within that species.

    Results

    Here we introduce PGV, a reference-agnostic representation of the pan-genome of a species based on the notion of consensus ordering. Our experimental results demonstrate that PGV enables an intuitive, effective and interactive visualization of a pan-genome by providing a genome browser that can elucidate complex structural genomic variations.

    Conclusions

    The PGV software can be installed via conda or downloaded fromhttps://github.com/ucrbioinfo/PGV. The companion PGV browser athttp://pgv.cs.ucr.educan be tested using example bed tracks available from the GitHub page.

     
    more » « less