From Mission Objectives to Design: An Efficient Framework for Downselection in Robotic Space Exploration

Abstract

One of the most critical tasks in any design process is the initial conversion of mission or program objectives into a baseline system architecture. Approaches to this task commonly rely on qualitative assessments of design options and detailed sizing of a handful of potential point designs. Mission value is often difficult to compare among alternatives because it is often not captured quantitatively. The framework presented here is unique in its quick and thorough population of a Pareto front in the mission importance vs. cost domain, allowing early selection of non-dominated, Pareto-optimal designs. This is achieved via automated evaluation of thousands of potential candidate architecture and payload combinations. In an illustration of this method, 70,000 cases are sized for a robotic mission to the near-Earth asteroid 99942 Apophis. A design on the resulting Pareto front is chosen, and initial mass and cost estimates returned are accurate within 2-5% compared to the detailed final design. Despite some limitations, it is concluded that this framework is theoretically extensible to non-robotic, and perhaps even non-exploration, missions. It is believed that this framework is a valuable addition to the system engineer’s toolbox and that it can allow the selection of higher-value, lower-cost solutions during preliminary design.