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Trajectory-based fast ball detection and tracking for an autonomous industrial robot system
KTH, School of Electrical Engineering and Computer Science (EECS).
2021 (English)In: International Journal of Intelligent Systems Technologies and Applications, ISSN 1740-8865, E-ISSN 1740-8873, Vol. 20, no 2, p. 126-145Article in journal (Refereed) Published
Abstract [en]

Autonomising industrial robots is the main goal in this paper; imagine humanoid robots that have several degrees of freedom (DOF) mechanisms as their arms. What if the humanoid's arms could be programmed to be responsive to their surrounding environment, without any hard-coding assigned? This paper presents the idea of an autonomous system, where the system observes the surrounding environment and takes action on its observation. The application here is that of rebuffing an object that is thrown towards a robotic arm's workspace. This application mimics the idea of high dynamic responsiveness of a robot's arm. This paper will present a trajectory generation framework for rebuffing incoming flying objects. The framework bases its assumptions on inputs acquired through image processing and object detection. After extensive testing, it can be said that the proposed framework managed to fulfil the real-time system requirements for this application, with an 80% successful rebuffing rate. 

Place, publisher, year, edition, pages
Inderscience Publishers , 2021. Vol. 20, no 2, p. 126-145
Keywords [en]
Depth image processing, Infrared image processing, Object detection, Object tracking, Ping-pong ball, Real-time, Serial robot, Stereo vision, Table tennis, Trajectory prediction, Anthropomorphic robots, Degrees of freedom (mechanics), Industrial robots, Infrared imaging, Object recognition, Real time systems, Tracking (position), Trajectories, Ping pong ball, Real- time, Serial robots, Surrounding environment, Table-tennis, Trajectory-based, Stereo image processing
National Category
Other Materials Engineering Control Engineering Biochemistry Molecular Biology
Identifiers
URN: urn:nbn:se:kth:diva-313210DOI: 10.1504/IJISTA.2021.119029Scopus ID: 2-s2.0-85120179029OAI: oai:DiVA.org:kth-313210DiVA, id: diva2:1665321
Note

QC 20220607

Available from: 2022-06-07 Created: 2022-06-07 Last updated: 2025-02-20Bibliographically approved

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AbdElKhalek, Y. M.
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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf