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

Rajeev Sharma, Steven M. LaValle, Seth Hutchinson

Research output: Contribution to journalArticlepeer-review


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 a problem of delivering parts 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, and design a state-feedback controller that optimizes the expected time that parts wait to be delivered. This leads to increased performance and a greater likelihood of stability in a manufacturing process. Six specific instances of the general framework are modeled and solved to yield optimal motion strategies for different robots operating under different assembly situations. Several extensions are also discussed.

Original languageEnglish (US)
Pages (from-to)160-174
Number of pages15
JournalIEEE Transactions on Robotics and Automation
Issue number2
StatePublished - 1996

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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