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Positioning error correction of autonomously movable robot arm

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Industrial robots are mainly used stationarily in one working position. SMEs often find themselves in situations where robots don’t have enough work to do, and because in general, robots cannot be easily moved to another position, the efficiency of robots will decrease. This study provides a solution for this issue. The solution can be found in a robot work cell where a mobile robot deals with robot arm transportation. However, since the mobile robot is not precise enough in positioning, machine vision is used to overcome this problem, which helps the robot to position itself accurately in relation to the work object. The solution has been developed and tested successfully at an Industry 4.0 testbed.
Słowa kluczowe
Rocznik
Strony
152--160
Opis fizyczny
Bibliogr. 13 poz., rys.
Twórcy
autor
  • Tallinn University of Technology (TalTech), Estonia
autor
  • TTK University of Applied Sciences, Tallinn, Estonia
autor
  • TTK University of Applied Sciences, Tallinn, Estonia
Bibliografia
  • [1] EUROBOTICS., 2013, Robotics 2020, Strategic Research Agenda for Robotics in Europa. https://www.eurobotics.net/cms/upload/topic_groups/SRA2020_SPARC.pdf
  • [2] WANG L., GAO R., VANCZA J., 2019, Symbiotic Human-Robot Collaborative Assembly, CIRP Ann., 68/2, 701–726.
  • [3] VAHER K., KANGRU T.; OTTO T., RIIVES J., 2019, The Mobility of Robotised Work Cells in Manufacturing, Proceedings of the 30th International DAAAM Symposium, Intelligent Manufacturing & Automation: 23–26th October 2019, Zadar, Croatia.
  • [4] PAIJENS A.F.M., HUANG L., AL-JUMAILY A.M., 2020, Implementation and Calibration of an Odometry System for Mobile Robots, Based on Optical Computer Mouse Sensors, Sensors and Actuators A: Physical, 301, 111731.
  • [5] CHEN I.M., 2001, Rapid Response Manufacturing Through a Rapidly Reconfigurable Robotic Workcell. Robotics and Computer-Integrated Manufacturing, 17/3, 199–213.
  • [6] FUJISHIMA M., MORI M., NARIMATSU K., IRINO N., 2019, Utilisation of IoT and Sensing for Machine Tools, Journal of Machine Engineering, 19/1, 38–47.
  • [7] DITTRICH M.A., DENKENA B., HAYTHEM B., UHLICH, F., 2019, Autonomous Machining – Recent Advances in Process Planning and Control, Journal of Machine Engineering, 19/1, 28–37.
  • [8] LU Y., 2017, Industry 4.0: A Survey on Technologies, Journal of Industrial Information Integration, Applications and Open Research, 6, 1–10, 10.1016/j.jii.2017.04.005.
  • [9] ROBLA-GOMEZ S., BECERRA V.M., LLATA J.R., 2017, Working Together: A Review on Safe Human-Robot Collaboration in Industrial Environments, Journals & Magazines IEEE Access. 5., 10.1109/ACCESS.2017. 2773127.
  • [10] LOUN K., RIIVES J., OTTO T., 2012, Workplace Performance and Capability Optimization in the Integrated Manufacturing, Proceedings of 8th International Conference of DAAAM Baltic Industrial Engineering, Tallinn.
  • [11] KUTS V., OTTO T., TÄHEMAA T., BONDARENKO Y., 2019, Digital Twin Based Synchronised Control and Simulation of the Industrial Robotic Cell Using Virtual Reality, Journal of Machine Engineering, 19/1, 128–145.
  • [12] SELL R., OTTO T., 2008, Remotely Controlled Multi Robot Environment, Proceedings of 19th EAEEIE Annual Conference, 20−25.10.1109/EAEEIE.2008.4610152., Tallinn.
  • [13] VAHER K., VAINOLA V., OTTO T., 2019, Industry 4.0 Laboratory, Industry 4.0, Technological Basis of Industy 4.0, 1/5, Proceedings IV International Scientific Conference, 24–27.06, Burgas, Bulgaria.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a7f0fa31-52c2-41fe-84b4-81fddf2932b2
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