Yoon, S.
and Bostelman, R.
(2020),
INFORMATION MODEL FOR A-UGV PERFORMANCE MEASUREMENT STANDARD, International Mechanical Engineering Congress & Exposition (IMECE) 2020, Portland, OR, US, [online], https://doi.org/10.1115/IMECE2020-24312, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930511
(Accessed February 6, 2025)
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INFORMATION MODEL FOR A-UGV PERFORMANCE MEASUREMENT STANDARD
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Abstract
Automatic through autonomous - Unmanned Ground Vehicles (A-UGVs) have the potential to be applied over a wide range of manufacturing systems under industry 4.0 paradigm. In order to use A-UGVs efficiently in a manufacturing system, it is necessary to select the A-UGV suitable for each factory or workplace. A framework for evaluating the A-UGV performance under a specific manufacturing environment is needed. ASTM International Committee F45 has been developing standards providing a basis for A-UGV manufacturers and users to compare tasks to the A-UGV capabilities. This paper proposes information models for A-UGV performance measurement along the standards development. The standard needs are analyzed to show how the standard and information model can be used for the introduction of A-UGVs into factories. The information model in this paper provides a structured way to describe the factory elements affecting the A-UGV performance, and the measured A-UGV performances against the factory elements. To validate the proposed information model, an A-UGV performance testbed was built and the information model instance is developed to describe the testbed elements. An A-UGV is tested against the testbed elements and the measured performance is described by the other instance. This paper contributes to mutual understanding, between A-UGV makers and users, to deliver A-UGV performance information efficiently and to provide basis for A-UGVs to be tested under the same conditions.Proceedings Title
International Mechanical Engineering Congress & Exposition (IMECE) 2020
Conference Dates
November 15-19, 2020
Conference Location
Portland, OR, US
Pub Type
Conferences
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Keywords
autonomous mobile robots, performance standard, navigation, obstacle avoidance, information model
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Created November 19, 2020, Updated January 21, 2022