OPTIMIZING ROBOT MOTION STRATEGIES FOR ASSEMBLY WITH STOCHASTIC-MODELS OF THE ASSEMBLY PROCESS

Gross-motion planning for assembly is commonly considered as a distinc t, isolated step between task sequencing/scheduling and fine-motion pl anning. Iri this paper we formulate a problem of delivering parts for assembly in a manner that integrates it with both the manufacturing pr ocess and the fine motions involved in the final assembly stages. One distinct characteristic of gross-motion planning for assembly is the p revalence of uncertainty involving time-in parts arrival, in request a rrival, etc. We propose a stochastic representation of the assembly pr ocess, and design a state-feedback controller that optimizes the expec ted time that parts wait to be delivered. This leads to increased perf ormance and a greater likelihood of stability in a manufacturing proce ss. Six specific instances of the general framework are modeled and so lved to yield optimal motion strategies for different robots operating under different assembly situations. Several extensions are also disc ussed.