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Autonomous Topography Detection and Traversal for an Inspection Robot Within the Beamline of Particle Accelerators

Schweizer, Nicolai ; Pongrac, Ivan (2021)
Autonomous Topography Detection and Traversal for an Inspection Robot Within the Beamline of Particle Accelerators.
9. International Particle Accelerator Conference (IPAC2018). Vancouver, Kanada (29.04.-04.05.2018)
doi: 10.26083/tuprints-00017646
Conference or Workshop Item, Secondary publication, Publisher's Version

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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: Autonomous Topography Detection and Traversal for an Inspection Robot Within the Beamline of Particle Accelerators
Language: English
Date: 2021
Place of Publication: Geneva
Year of primary publication: 2018
Publisher: JACoW Publishing
Book Title: Proceedings of the 9th International Particle Accelerator Conference
Event Title: 9. International Particle Accelerator Conference (IPAC2018)
Event Location: Vancouver, Kanada
Event Dates: 29.04.-04.05.2018
DOI: 10.26083/tuprints-00017646
Corresponding Links:
Origin: Secondary publication service
Abstract:

Particle accelerators feature ultra-high vacuum pipe systems with unique topography, i.e. with a multitude of different vacuum chambers of varying dimensions and varying pipe apertures. In order to be able to examine the interior of the entire vacuum system, even those parts which are not accessible without disassembling large parts of the accelerator, a semi-autonomous robot is being developed which shall traverse and visually inspect the vacuum system of particle accelerators. We present a generic concept based on distance sensors for the inspection robot to detect steps between vacuum chambers and gaps in the beamline. Movement strategies to autonomously overcome these basic obstacles are introduced. For evaluation we use simulations of ideal environments with flat surfaces as well as realistic beam pipe environments of the SIS100 particle accelerator. Additionally, a prototype of our robot concept confirms the implementation of all maneuvers. Results show that obstacles of previously unknown dimensions can be detected and reliably traversed.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-176463
Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Robotics (from 01.08.2022 renamed Control Methods and Intelligent Systems)
Date Deposited: 07 Apr 2021 08:44
Last Modified: 28 Jul 2023 09:31
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/17646
PPN: 478852398
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