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

Gross-motion planning for assembly is commonly considered as a distinct, isolated step between task sequencing/scheduling and ne-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 ne motions involved in the nal 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 speciic instances of the general framework are modeled and solved to yield optimal motion strategies for diierent robots operating under diierent assembly situations. Several extensions are also discussed.