Asking for Help through Adaptable Autonomy in Robotic Search and Rescue

Robotic search and rescue teams of the future will consist of both robots and human operators. Operators are utilized for identifying victims, by means of camera feeds from the robot, and for helping with navigation when autonomy is insufficient. As the size of these robot teams increases, the mental workload on operators increases, and robots find themselves in precarious situations with no assistance for resolution. This paper presents an approach that utilizes multiple levels of autonomy to allow a robot to consider a range of options, including asking for operator assistance, for dealing with problematic situations to maximize efficient use of the operator’s time. Individual robots use self monitoring to determine failures in task progression, a form of local autonomy. Upon this trigger, the robot evaluates decisions to properly route asking for help, consensus autonomy. A Call Center alerts the operator(s) to incoming requests for assistance. This results in a better use of operator time by focusing attention where it is needed. Experiments explore the effectiveness of agents’ decisions, both local and team-level, with multiple simulators. A high fidelity simulator and user interface further evaluate how effective robot information is relayed to the operator, through human trials.