STRATA: A Unified Framework for Task Assignments in Large Teams of Heterogeneous Robots

Large teams of robots have the potential to solve complex multi-task problems that are intractable for a single robot working independently. However, solving complex multi-task problems requires leveraging the relative strengths of different robots in the team. We present Stochastic TRAit-based Task Assignment (STRATA), a unified framework that models large teams of heterogeneous robots and performs optimal task assignments. Specifically, given information on which traits (capabilities) are required for various tasks, STRATA computes the optimal assignments of robots to tasks such that the task-trait requirements are achieved. Inspired by prior work in robot swarms and biodiversity, we categorize robots into different species (groups) based on their traits. We model each trait as a continuous variable and differentiate between traits that can and cannot be aggregated from different robots. STRATA is capable of reasoning about both species-level and robot-level differences in traits. Further, we define measures of diversity for any given team based on the team's continuous-space trait model. We illustrate the necessity and effectiveness of STRATA using detailed simulations and a capture the flag game environment.