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 fine-motion planning. In this paper the authors 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. The authors 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.