Process and Performance in Human-Robot Teams

Abstract

The authors are developing a theory for human control of robot teams based on considering how control difficulty grows with team size. Current work focuses on domains, such as foraging, in which robots perform largely independent tasks. Such tasks are particularly amenable to analysis because effects on performance and cognitive resources are predicted to be additive, and tasks can safely be allocated across operators because of their independence. The present study addresses the interaction between automation and organization of human teams in controlling large robot teams performing an urban search-and-rescue (USAR) task. Two possible ways to organize operators were identified: as individual assignments of robots to operators, assigned robots, or as a shared pool in which operators service robots from the population as needed. The experiment compares two-person teams of operators controlling teams of 12 robots each in the assigned-robots condition or sharing control of 24 robots in the shared-pool condition using either waypoint control in the manual condition or autonomous path planning in the autonomy condition. Automating path planning improved system performance, but process measures suggest it may weaken situation awareness.