Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Enabling Robot Agility in Manufacturing Kitting Applications

Published

Author(s)

Zeid Kootbally, Craig I. Schlenoff, Brian Antonishek, Frederick M. Proctor, Thomas Kramer, William Harrison, Satyandra K. Gupta

Abstract

For the most part, robots perform best in highly structured environments, where objects are in well-known, predictable loca-tions. Another way to describe this is that robots are not considered agile. But, in order for them to be useful to small manu-facturers and to also allow larger manufacturers to offer more automated customization of high volume parts, they need to be. In this paper, we describe various technologies that are being developed at the National Institute of Standards and Technology (NIST) in conjunction with outside organizations, such as IEEE, which can be used to enhance the agility of manufacturing robot systems. We validate these technologies using two industrially-relevant use cases. The first deals with task failure identification and recovery and the second deals with robot dynamic retasking. These use cases were successfully performed using a formal knowledge representation, a graph database, a perception system, a high-level and low-level planning system, as well as an overall architecture which brought all of the components together.
Citation
Journal of Integrated Computer-Aided Engineering
Volume
25

Keywords

manufacturing robotics, agility, kitting, ontology, rapid retasking

Citation

Kootbally, Z. , Schlenoff, C. , Antonishek, B. , Proctor, F. , Kramer, T. , Harrison, W. and Gupta, S. (2018), Enabling Robot Agility in Manufacturing Kitting Applications, Journal of Integrated Computer-Aided Engineering, [online], https://doi.org/10.3233/ICA-180566 (Accessed June 19, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created March 14, 2018, Updated March 3, 2022