Predicting Visual Differentiability for Unmanned Aerial Vehicle Gestures
Predicting Visual Differentiability for Unmanned Aerial Vehicle Gestures
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DOI：
10.1109/lra.2022.3180414
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发表时间：
2022-10
影响因子：
5.2
通讯作者：
P. Fletcher;Angeline Luther;Carrick Detweiler;Brittany A. Duncan
中科院分区：
文献类型：
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作者：
P. Fletcher;Angeline Luther;Carrick Detweiler;Brittany A. Duncan
无人驾驶飞行器(UAV)越来越多地融入各种人类交互领域，这就需要强大的人-机器人通信系统。可视通信技术在向观察者传达具体信息的能力方面显示出了希望。这类技术通常被描述为无人机的“手势”，在无人驾驶空中飞行领域可能特别有用，因为它们可以被集成为一个独立的软件解决方案，而基于灯光或声音的系统通常需要额外的硬件，并增加飞行器的重量。在远距离操作降低了基于声音的通信策略的有效性的情况下，手势也可能有用。由于手势是一种视觉交流技术，因此手势被设计为优化观察者视觉感知手势运动形状的能力是至关重要的。诸如集合内手势之间的视觉分辨率低的因素可能会降低观察者对手势运动的形状进行分类的能力。在这封信中，我们讨论了多项手势感知调查的结果。我们还开发和评估技术来提前预测参与者可能如何感知无人机手势。我们证明了参与者手势分类的准确性与轨迹距离度量相关，并提出了一种评估高可分度手势集的方法。这封信将使手势设计师能够创建可高度自信地区分的手势集。
Unmanned Aerial Vehicles (UAVs) are increasingly integrated into diverse human interaction domains that require robust human-robot communication systems. Visual communication techniques have shown promise in their ability to communicate concrete information to observers. Such techniques, often described as a UAV ‘gesture,’ may be especially useful in the domain of unmanned aerial flight as they can be integrated as a stand-alone software solution in contrast to light or sound-based systems that often require additional hardware and add weight to a vehicle. Gestures may also be useful in contexts where long distance operation reduces the effectiveness of sound-based communication strategies. As gesture is a visual communication technique, it is critical that gestures are designed to optimize an observer’s ability to visually perceive the shape of a gesture’s motion. Factors such as low visual differentiability between gestures within a set may reduce an observer’s ability to classify the shape of a gestural motion. In this letter, we discuss the results from multiple gesture perception surveys. We also develop and evaluate techniques to predict, in advance, how participants may perceive a UAV gesture. We demonstrate that participant gesture classification accuracy correlates to trajectory distance measures and present a method for evaluating high-differentiabilty gesture sets. This letter will enable gesture designers to create gesture sets that are differentiable with high-confidence.