Adaptive Event Detection and Nonverbal Communication for Human Supervision of Multi-Robot Teams

This thesis investigates how robots communicate with human supervisors during human–robot teaming. Supervising multiple autonomous robots can be cognitively demanding, so this research examines how robots can better decide what information to communicate and how to present it. The thesis studies how sounds, visuals, and combined audio-visual cues affect a person’s awareness of robot activity. It also develops a method that allows supervisors to adapt what robots detect using natural language descriptions, rather than fixed rules. These approaches support more flexible and effective robot systems for tasks such as search and rescue and exploration.