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Presentation
Virtual Operator Modeling Approach for Construction Machinery
Proceedings of the 2015 Conference on Autonomous and Robotic Construction of Infrastructure
  • Yu Du, Iowa State University
  • Michael C. Dorneich, Iowa State University
  • Brian L. Steward, Iowa State University
  • Eric R. Anderson, Deere & Company
  • Lawrence F. Kane, Deere & Company
  • Brian J. Gilmore, Deere & Company
Document Type
Conference Proceeding
Publication Version
Published Version
Publication Date
1-1-2015
Conference Title
Conference on Autonomous and Robotic Construction of Infrastructure
Conference Date
June 2-3, 2015
Geolocation
(42.0307812, -93.63191310000002)
Abstract

Greater understanding of how highly skilled operators achieve high machine performance and productivity can inform the development of automation technology for construction machinery. Current human operator models, however, have limited fidelity and may not be useful for machinery automation. In addition, while physical modeling and simulation is widely employed in product development, current operator simulation models may be a limiting factor in assessing the performance envelope of virtual prototypes. A virtual operator modelling approach for construction machinery was developed. Challenges to the development of human operator models include determining what cues and triggers human operators use, how human operators make decisions, and how to account for the diversity of human operator responses. Operator interviews were conducted to understand and build a framework of tasks, strategies, cues, and triggers that operators commonly use while controlling a machine through a repeating work cycle. In particular, a set of operation data were collected during an excavator trenching operation and were analyzed to classify tasks and strategies. A rule base was derived from interview and data analyses. Common nomenclature was defined and is explained. Standard tasks were derived from operator interviews, which led to the development of task classification rules and algorithm. Task transitions were detected with fuzzy transition detection classifiers.

Comments

This is a paper from Proceedings of the 2015 Conference on Autonomous and Robotic Construction of Infrastructure, which can be found in full at: http://lib.dr.iastate.edu/intrans_reports/141/.

Copyright Owner
Iowa State University
Language
en
File Format
application/pdf
Citation Information
Yu Du, Michael C. Dorneich, Brian L. Steward, Eric R. Anderson, et al.. "Virtual Operator Modeling Approach for Construction Machinery" Ames, IAProceedings of the 2015 Conference on Autonomous and Robotic Construction of Infrastructure (2015) p. 56 - 66
Available at: http://works.bepress.com/brian_steward/67/