Evidence map›Paper›PMID 38931576›Full record

ArticleSensors (Basel, Switzerland)2024

Development of an Artificial Vision for a Parallel Manipulator Using Machine-to-Machine Technologies.

Arailym Nussibaliyeva, Gani Sergazin, Gulzhamal Tursunbayeva, Arman Uzbekbayev, Nursultan Zhetenbayev, Yerkebulan Nurgizat, Balzhan Bakhtiyar, Sandugash Orazaliyeva, Saltanat Yussupova

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Arailym NussibaliyevaDepartment of Electronics and Robotics, Almaty University of Power Engineering and Telecommunications named Gumarbek Daukeyev, Almaty 050013, Kazakhstan.
Gani SergazinAcademy of Logistics and Transport, Almaty 050012, Kazakhstan.
Gulzhamal TursunbayevaDepartment of Information Security, Eurasian National University, Astana 10000, Kazakhstan.
Arman UzbekbayevResearch Institut of Applied Science and Technologies, Almaty 050013, Kazakhstan.ORCID 0009-0003-6728-0748
Nursultan ZhetenbayevDepartment of Electronics and Robotics, Almaty University of Power Engineering and Telecommunications named Gumarbek Daukeyev, Almaty 050013, Kazakhstan.ORCID 0000-0003-3358-1182
Yerkebulan NurgizatDepartment of Electronics and Robotics, Almaty University of Power Engineering and Telecommunications named Gumarbek Daukeyev, Almaty 050013, Kazakhstan.ORCID 0000-0002-9712-5592
Balzhan BakhtiyarDepartment of Heat Power Engineering, NCJSC S.Seifullin Kazakh Agro Technical Research University, Astana 10000, Kazakhstan.
Sandugash OrazaliyevaDepartment of Electronics and Robotics, Almaty University of Power Engineering and Telecommunications named Gumarbek Daukeyev, Almaty 050013, Kazakhstan.ORCID 0000-0002-2838-1867
Saltanat YussupovaDepartment of Electronics and Robotics, Almaty University of Power Engineering and Telecommunications named Gumarbek Daukeyev, Almaty 050013, Kazakhstan.

Funding

CSRD VA 1
6 · The paper itself

Abstract

This research focuses on developing an artificial vision system for a flexible delta robot manipulator and integrating it with machine-to-machine (M2M) communication to optimize real-time device interaction. This integration aims to increase the speed of the robotic system and improve its overall performance. The proposed combination of an artificial vision system with M2M communication can detect and recognize targets with high accuracy in real time within the limited space considered for positioning, further localization, and carrying out manufacturing processes such as assembly or sorting of parts. In this study, RGB images are used as input data for the MASK-R-CNN algorithm, and the results are processed according to the features of the delta robot arm prototype. The data obtained from MASK-R-CNN are adapted for use in the delta robot control system, considering its unique characteristics and positioning requirements. M2M technology enables the robot arm to react quickly to changes, such as moving objects or changes in their position, which is crucial for sorting and packing tasks. The system was tested under near real-world conditions to evaluate its performance and reliability.

Indexed as

artificial vision systemdelta robot manipulatormachine-to-machine communicationMASK-R-CNNRGB

Identifiers

PMID38931576
PMCPMC11207595

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.