STOCHASTIC DYNAMIC-PROGRAMMING APPLIED TO PLANNING OF ROBOT GRINDING TASKS

This paper proposes an intelligent manufacturing system that can make decisions about the process in light of the uncertain outcome of these decisions and attempts to minimize the expected economic penalty resu lting from those decisions. It uses robot weld bead grinding as an exa mple of a process with significant process variation. The need for mul tiple grinding passes, the poor predictability of those passes, the ta sk requirements, and the process constraints conspire to make planning and controlling weld bead grinding a formidable problem. A three tier hierarchical control system is proposed to plan an optimal sequence o f grinding passes, dynamically simulate each pass, execute the planned sequence of controlled grinding passes, and modify the pass sequence as grinding continues. The top tier, described in this paper, plans th e grinding sequence for each weld bead, and is implemented using Stoch astic Dynamic Programming, selecting the volumetric removal and feedsp eed for each pass in order to optimize the satisfaction of the task re quirements by the entire grinding sequence within the equipment, task, and process constraints. The resulting optimal policies have quite co mplex structures, showing foresight, anxiety, indifference, and aggres siveness, depending upon the situation.