Assessing the Impact of Alerts on the Human Supervisor’s Decision-Making Performance in Multi-Robot Missions

Abstract

Multi-robot teams can be very useful in a wide variety of search and rescue missions in challenging environments. In a mission with considerable uncertainty due to intermittent communications, degraded information flow, and failures, humans need to assess both the current and expected future states and update task assignments in human-robot teams as quickly as possible. We have developed an alert generation framework that can perform risk assessment and robot tasking suggestions to assist human supervisors. Our approach for task assignment suggestion generation combines heuristics-based task selection with forward simulation-based probabilistic assessment. As the characteristics of decision aids can largely vary human performance, an alert system may or may not improve decision-making. We aim to configure our framework with a goal to improve human decision-making performance. Towards that, we present some preliminary user studies and design reasoning, which informed our final comprehensive human subject study. We demonstrate in the study that supervisors can improve their decision-making abilities, make faster decisions, and increase mission performance by using our alert generation framework. Our empirical findings also show that our framework does not require significant training and that people with a higher level of trust in automation perform better when provided with alerts. We also find that people with certain personality traits such as high agreeableness and conscientiousness are the most benefited by alerts.