Methods of automated detection of travel points when training a collaborative robot
An algorithm has been developed and implemented in this paper, which allows automating the process of forming and controlling scenarios for the movement of a collaborative robot (“Cobot”) through a database of points without specific interfaces, services, and software tools characteristic of each Co...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/03/bioconf_aquaculture2024_02002.pdf |
| _version_ | 1797352970505945088 |
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| author | Evstifeeva N.A. Gurdiumov S.A. Kleimenov A.A. Gerasimova A.A. |
| author_facet | Evstifeeva N.A. Gurdiumov S.A. Kleimenov A.A. Gerasimova A.A. |
| author_sort | Evstifeeva N.A. |
| collection | DOAJ |
| description | An algorithm has been developed and implemented in this paper, which allows automating the process of forming and controlling scenarios for the movement of a collaborative robot (“Cobot”) through a database of points without specific interfaces, services, and software tools characteristic of each Cobot model. The unification of the developed single graphical interface is achieved by automating the work with Cobot controllers through specialised structured file formats and Robot Operation System (ROS), and by automatically detecting marks as movement points in the image received from the stereo camera using neural network-based models and image processing techniques. Research based on a series of experiments ensured the selection of the most effective image processing method and neural network model in terms of accuracy, speed, resource consumption. The approach formalised in the paper and the graphical interface allowed to implement a classical set of industrial tasks of Cobot motion control. |
| first_indexed | 2024-03-08T13:24:33Z |
| format | Article |
| id | doaj.art-56f7109c816c4de1b50d94689d60857c |
| institution | Directory Open Access Journal |
| issn | 2117-4458 |
| language | English |
| last_indexed | 2024-03-08T13:24:33Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj.art-56f7109c816c4de1b50d94689d60857c2024-01-17T15:00:14ZengEDP SciencesBIO Web of Conferences2117-44582024-01-01840200210.1051/bioconf/20248402002bioconf_aquaculture2024_02002Methods of automated detection of travel points when training a collaborative robotEvstifeeva N.A.0Gurdiumov S.A.1Kleimenov A.A.2Gerasimova A.A.3National University of Science and Technology MISiSNational University of Science and Technology MISiSNational University of Science and Technology MISiSNational University of Science and Technology MISiSAn algorithm has been developed and implemented in this paper, which allows automating the process of forming and controlling scenarios for the movement of a collaborative robot (“Cobot”) through a database of points without specific interfaces, services, and software tools characteristic of each Cobot model. The unification of the developed single graphical interface is achieved by automating the work with Cobot controllers through specialised structured file formats and Robot Operation System (ROS), and by automatically detecting marks as movement points in the image received from the stereo camera using neural network-based models and image processing techniques. Research based on a series of experiments ensured the selection of the most effective image processing method and neural network model in terms of accuracy, speed, resource consumption. The approach formalised in the paper and the graphical interface allowed to implement a classical set of industrial tasks of Cobot motion control.https://www.bio-conferences.org/articles/bioconf/pdf/2024/03/bioconf_aquaculture2024_02002.pdf |
| spellingShingle | Evstifeeva N.A. Gurdiumov S.A. Kleimenov A.A. Gerasimova A.A. Methods of automated detection of travel points when training a collaborative robot BIO Web of Conferences |
| title | Methods of automated detection of travel points when training a collaborative robot |
| title_full | Methods of automated detection of travel points when training a collaborative robot |
| title_fullStr | Methods of automated detection of travel points when training a collaborative robot |
| title_full_unstemmed | Methods of automated detection of travel points when training a collaborative robot |
| title_short | Methods of automated detection of travel points when training a collaborative robot |
| title_sort | methods of automated detection of travel points when training a collaborative robot |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2024/03/bioconf_aquaculture2024_02002.pdf |
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