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Camera Arrangement Optimization for Workspace Monitoring in Human-Robot Collaboration

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F23%3A10250925" target="_blank" >RIV/61989100:27230/23:10250925 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.mdpi.com/1424-8220/23/1/295" target="_blank" >https://www.mdpi.com/1424-8220/23/1/295</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/s23010295" target="_blank" >10.3390/s23010295</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Camera Arrangement Optimization for Workspace Monitoring in Human-Robot Collaboration

  • Original language description

    Human-robot interaction is becoming an integral part of practice. There is a greater emphasis on safety in workplaces where a robot may bump into a worker. In practice, there are solutions that control the robot based on the potential energy in a collision or a robot re-planning the straight-line trajectory. However, a sensor system must be designed to detect obstacles across the human-robot shared workspace. So far, there is no procedure that engineers can follow in practice to deploy sensors ideally. We come up with the idea of classifying the space as an importance index, which determines what part of the workspace sensors should sense to ensure ideal obstacle sensing. Then, the ideal camera positions can be automatically found according to this classified map. Based on the experiment, the coverage of the important volume by the calculated camera position in the workspace was found to be on average 37% greater compared to a camera placed intuitively by test subjects. Using two cameras at the workplace, the calculated positions were 27% more effective than the subjects&apos; camera positions. Furthermore, for three cameras, the calculated positions were 13% better than the subjects&apos; camera positions, with a total coverage of more than 99% of the classified map.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20204 - Robotics and automatic control

Result continuities

  • Project

    <a href="/en/project/EF17_049%2F0008425" target="_blank" >EF17_049/0008425: A Research Platform focused on Industry 4.0 and Robotics in Ostrava Agglomeration</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Sensors

  • ISSN

    1424-3210

  • e-ISSN

    1424-8220

  • Volume of the periodical

    23

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    19

  • Pages from-to

    1-19

  • UT code for WoS article

    000909987000001

  • EID of the result in the Scopus database

    2-s2.0-85145981166