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Distributed multi-agent unmanned aerial systems (UAS) have the potential to be heavily utilized in environmental monitoring, especially in wetland monitoring. Deep active learning algorithms provide key tools to analyze the sensed images captured during monitoring and interpret them precisely. However, these algorithms demand significant computational resources that limit their use with distributed UAS. In this paper, we propose a novel algorithm for consensus-enabled active learning that drastically reduces the computational demand while increasing the overall model accuracy. Once each of the UAS obtains a labeled subset of images through active learning, we update the weights of the model for three epochs only on the new images to reduce the computational cost, allowing for an increased operational time. The group of UAS communicates the model weights instead of the raw data and then leverages consensus to agree on updated weights. The consensus step mitigates the impact on weights caused by the updates and generalizes the knowledge of each individual UAS to the whole system, which results in increased model accuracy. Our method achieved an average of 11.15% increase in accuracy over 25 acquisition iterations whilst utilizing only 16.8% of the processor time compared to the centralized method of active learning. 

Unmanned aerial vehicles (UAVs) are becoming more common, presenting the need for effective human-robot communication strategies that address the unique nature of unmanned aerial flight. Visual communication via drone flight paths, also called gestures, may prove to be an ideal method. However, the effectiveness of visual communication techniques is dependent on several factors including an observer's position relative to a UAV. Previous work has studied the maximum line-of-sight at which observers can identify a small UAV [1]. However, this work did not consider how changes in distance may affect an observer's ability to perceive the shape of a UAV's motion. In this study, we conduct a series of online surveys to evaluate how changes in line-of-sight distance and gesture size affect observers' ability to identify and distinguish between UAV gestures. We first examine observers' ability to accurately identify gestures when adjusting a gesture's size relative to the size of a UAV. We then measure how observers' ability to identify gestures changes with respect to varying line-of-sight distances. Lastly, we consider how altering the size of a UAV gesture may improve an observer's ability to identify drone gestures from varying distances. Our results show that increasing the gesture size across varying UAV to gesture ratios did not have a significant effect on participant response accuracy. We found that between 17 m and 75 m from the observer, their ability to accurately identify a drone gesture was inversely proportional to the distance between the observer and the drone. Finally, we found that maintaining a gesture's apparent size improves participant response accuracy over changing line-of-sight distances. 

Unmanned Aerial Vehicle (UAV) flight paths have been shown to communicate meaning to human observers, similar to human gestural communication. This paper presents the results of a UAV gesture perception study designed to assess how observer viewpoint perspective may impact how humans perceive the shape of UAV gestural motion. Robot gesture designers have demonstrated that robots can indeed communicate meaning through gesture; however, many of these results are limited to an idealized range of viewer perspectives and do not consider how the perception of a robot gesture may suffer from obfuscation or self-occlusion from some viewpoints. This paper presents the results of three online user-studies that examine participants' ability to accurately perceive the intended shape of two-dimensional UAV gestures from varying viewer perspectives. We used a logistic regression model to characterize participant gesture classification accuracy, demonstrating that viewer perspective does impact how participants perceive the shape of UAV gestures. Our results yielded a viewpoint angle threshold from beyond which participants were able to assess the intended shape of a gesture's motion with 90% accuracy. We also introduce a perceptibility score to capture user confidence, time to decision, and accuracy in labeling and to understand how differences in flight paths impact perception across viewpoints. These findings will enable UAV gesture systems that, with a high degree of confidence, ensure gesture motions can be accurately perceived by human observers. 

Type annotations connect variables to domain-specific types. They enable the power of type checking and can detect faults early. In practice, type annotations have a reputation of being burdensome to developers. We lack, however, an empirical understanding of how and why they are burdensome. Hence, we seek to measure the baseline accuracy and speed for developers making type annotations to previously unseen code. We also study the impact of one or more type suggestions. We conduct an empirical study of 97 developers using 20 randomly selected code artifacts from the robotics domain containing physical unit types. We find that subjects select the correct physical type with just 51% accuracy, and a single correct annotation takes about 2 minutes on average. Showing subjects a single suggestion has a strong and significant impact on accuracy both when correct and incorrect, while showing three suggestions retains the significant benefits without the negative effects. We also find that suggestions do not come with a time penalty. We require subjects to explain their annotation choices, and we qualitatively analyze their explanations. We find that identifier names and reasoning about code operations are the primary clues for selecting a type. We also examine two state-of-the-art automated type annotation systems and find opportunities for their improvement.