Predicting Visual Differentiability for Unmanned Aerial Vehicle Gestures*

Paul Fletcher, Angeline Luther, Carrick Detweiler, Brittany Duncan

Research output: Contribution to journalArticlepeer-review

Abstract

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 soundbased 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 work, 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 work will enable gesture designers to create gesture sets that are differentiable with high-confidence.

Original languageEnglish (US)
Pages (from-to)1-8
Number of pages8
JournalIEEE Robotics and Automation Letters
DOIs
StateAccepted/In press - 2022

Keywords

  • Autonomous aerial vehicles
  • Design and Human Factors
  • Gesture
  • Human-Robot Collaboration
  • Observers
  • Posture and Facial Expressions
  • Robots
  • Shape
  • Task analysis
  • Trajectory
  • Visualization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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