Choosing Autonomy Modes for Multirobot Search

OBJECTIVE The number of robots an operator can supervise increases with the robots' level of autonomy. The reported study investigates multirobot foraging to identify aspects of the task most suitable for automation. BACKGROUND Many envisioned applications of robotics involve multirobot teams. One of the simplest of these applications is foraging, in which robots are operated independently to explore and discover targets. Depending on levels of autonomy and task, operators have been found able to manage 3 to 12 robots. METHOD The foraging task can be functionally subdivided into visiting new regions and identifying targets. In the reported experiment, full-task foraging performance was compared with exploration and perceptual search performance for 4-, 8-, and 12-robot teams in a between-groups repeated measures design. RESULTS Operators in the full-task condition could not successfully manage 12 robots, finding only half as many victims as perceptual search operators. Exploration performance was roughly the same in the full-task and exploration conditions, suggesting that performance of this subtask was limiting the number of robots that could be controlled. CONCLUSION Performance and workload measures indicate that exploration (navigation) tasks are the limiting factor in multirobot foraging. This finding suggests that robot navigation is the best candidate for automation. APPLICATION Search tasks, such as foraging or perimeter control, account for many of the near-term applications envisioned for multirobot teams. The results support the choice of task-centered architectures in which the control and coordination of robotic platforms is automated, leaving search and identification of targets to human operators.