Optimizing robot motion strategies for assembly with stochastic models of the assembly process

Rajeev Sharma, Steven M. LaValle, Seth A. Hutchinson

Research output: Contribution to conferencePaper

Abstract

Gross-motion planning for assembly is commonly considered as a distinct, isolated step between task sequencing/scheduling and fine-motion planning. In this paper we formulate the problem of gross-motion planning for assembly in a manner that integrates it with both the manufacturing process and the fine motions involved in the final assembly stages. One distinct characteristic of gross-motion planning for assembly is the prevalence of uncertainty involving time - in parts arrival, in request arrival, etc. We propose a stochastic representation of the assembly process that improves the robot performance in the uncertain assembly environment by optimizing an appropriate criterion in the expected sense.

Original languageEnglish (US)
Pages341-346
Number of pages6
StatePublished - Jan 1 1995
EventProceedings of the IEEE International Symposium on Assembly and Task Planning - Pittsburgh, PA, USA
Duration: Aug 10 1995Aug 11 1995

Other

OtherProceedings of the IEEE International Symposium on Assembly and Task Planning
CityPittsburgh, PA, USA
Period8/10/958/11/95

ASJC Scopus subject areas

  • Engineering(all)

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    Sharma, R., LaValle, S. M., & Hutchinson, S. A. (1995). Optimizing robot motion strategies for assembly with stochastic models of the assembly process. 341-346. Paper presented at Proceedings of the IEEE International Symposium on Assembly and Task Planning, Pittsburgh, PA, USA, .