This paper presents an approach to using a large team of UAVs to find radio frequency (RF) emitting targets in a large area. Small, inexpensive UAVs that can collectively and rapidly determine the approximate location of intermittently broadcasting and mobile RF emitters have a range of applications in both military, e.g., for finding SAM batteries, and civilian, e.g., for finding lost hikers, domains. Received Signal Strength Indicator (RSSI) sensors on board the UAVs measure the strength of RF signals across a range of frequencies. The signals, although noisy and ambiguous due to structural noise, e.g., multipath effects, overlapping signals and sensor noise, allow estimates to be made of emitter locations. Generating a probability distribution over emitter locations requires integrating multiple signals from different UAVs into a Bayesian filter, hence requiring cooperation between the UAVs. Once likely target locations are identified, EO-camera equipped UAVs must be tasked to provide a video stream of the area to allow a user to identify the emitter.
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.
As mini-UAVs become more capable and reliable, it is important to start looking at the factors differentiating them from other classes of unmanned vehicles. One such factor is the physical proximity of operators to the vehicle during deployment. Operators of these UAVs are often within sight of their vehicle, and share many environmental cues such as visual landmarks. However, operating in the field also entails additional environmental stresses, such as less optimal use of computer equipment, variations in weather, and the physical demands of the terrain. In this paper, a pilot study is conducted to determine if any of these factors significantly impact situation awareness, by comparing operator performance in a visual identification task in a live field test with operators performing an identical task in a lab environment. Metric results suggest that performance is similar across the two conditions, but qualitative responses from participants suggest that the underlying strategies employed differ in the two conditions.
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