In this paper, we present an asynchronous display method, coined image queue, which allows operators to search through a large amount of data gathered by autonomous robot teams. We discuss and investigate the advantages of an asynchronous display for foraging tasks with emphasis on Urban Search and Rescue. The image queue approach mines video data to present the operator with a relevant and comprehensive view of the environment in order to identify targets of interest such as injured victims. It fills the gap for comprehensive and scalable displays to obtain a network-centric perspective for UGVs. We compared the image queue to a traditional synchronous display with live video feeds and found that the image queue reduces errors and operator's workload. Furthermore, it disentangles target detection from concurrent system operations and enables a call center approach to target detection. With such an approach we can scale up to very large multi-robot systems gathering huge amounts of data that is then distributed to multiple operators.

Controlling a team of Unmanned Aerial Vehicles (UAV) requires the operator to perform continuous surveillance and path planning. The operator's situation awareness is likely to degrade as an increasing number of surveillance videos must be viewed and integrated. The Picture-in-Picture display (PiP) provides one solution for integrating multiple UAV camera video by allowing the operator to view the video feed in the context of surrounding terrain. The experimental SUAVE (Simple Unmanned Areal Vehicle Environment) display extends PiP methods by sampling imagery from the video stream to texture a 3D map of the terrain. The operator can then inspect this imagery using world in miniature (WIM) or fly-through methods. We investigate the properties and advantages of SUAVE in the context of a search mission with 11 UAVs finding a strong advantage for finding targets While performance is expected to improve with increasing numbers of UAVs we did not find differences in performance between models generated by 11 UAVs and those employing 22 UAVs.