A Computational Framework for Assessing Mission Outcomes with Humans and Robots

Space exploration is progressing toward long-term missions that involve both human (HAs) and robotic agents (RAs) in operations in lunar space habitats, the Gateway space station, and the moon-to-Mars program. These missions require high-level intelligence and a sustained performance over extended periods. Analyzing agent performance solely at the task level is insufficient for such complex applications because the resources consumed by agents are coupled with the utility they provide under various conditions. Additionally, factors such as the availability of agents to respond to hazardous events, impacted by factors including human sleep cycles and robot charging times, must be considered. Understanding how resources, utility, and availability are interrelated is crucial for early-phase decision making, assessing logistics, and steering investments in promising directions. In this study, the rapid simulation capabilities of control-oriented dynamic computational modeling (CDCM) were used to explore the trade space involving an HA and an RA tasked with maintaining a smart space habitat. This approach was used to model two independent parallel scenarios as systems of systems that use stochastic methods to account for mission variabilities. A human scientist (HS) was included to quantify the mission’s research outcomes. The outcomes generated by the HS served as a metric to compare the performance of the agents along with the costs associated with engaging the HA and RA.