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Analysis of a gesture-based interface for UAV flight path generation

机译:用于无人机飞行路径生成的基于手势的界面分析

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Traditionally, flight paths for unmanned aerial vehicles (UAVs) are generated offline by trained pilots and engineers using assumed environmental conditions, terrain and obstacles as constraints. As new applications for UAVs emerge, their user base shifts from one of operators with knowledge of low level systems to that of non-experts. These new operators require a more intuitive method for building desired UAV flight paths such that they can leverage the full capabilities of the vehicle without needing to understand its system complexities. We present a gesture-based natural language interface for defining trajectory segments using a library of twelve simple hand gestures. A user study is presented to analyze the effectiveness, ease-of-use and accuracy of the gesture-based interface as compared to a baseline mouse interface. We explore differences seen between subjects given their hand dominance, their prior UAV flight experience (or lack thereof), and whether they chose to sit or stand while using the gesture interface. Given limited training time, subjects were able to accurately define an average of 74.36% of trajectory segments. Overall the user study highlights the favorable potential for the use of the gesture-based interface as an alternative input modality, as well as, feedback for future interface improvements and training methods.
机译:传统上,无人驾驶飞机(UAV)的飞行路径是由受过训练的飞行员和工程师使用假定的环境条件,地形和障碍物作为约束条件离线生成的。随着无人机新应用的出现,他们的用户基础从了解低级系统的运营商之一转变为非专家。这些新的运营商需要一种更直观的方法来构建所需的无人机飞行路径,以便他们可以利用车辆的全部功能,而无需了解其系统的复杂性。我们提出了一种基于手势的自然语言界面,用于使用十二个简单手势库定义轨迹段。提出了一项用户研究,以分析与基线鼠标界面相比基于手势的界面的有效性,易用性和准确性。我们探讨了在主体处于优势地位,先前的无人机飞行经验(或缺乏经验)以及使用手势界面时是选择坐着还是站着之间,受试者之间的差异。在有限的训练时间下,受试者能够准确定义平均74.36%的轨迹段。总的来说,用户研究突出了使用基于手势的界面作为替代输入方式的有利潜力,以及对未来界面改进和培训方法的反馈。

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