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 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.
Original language | English (US) |
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Pages (from-to) | 11799-11806 |
Number of pages | 8 |
Journal | IEEE Robotics and Automation Letters |
Volume | 7 |
Issue number | 4 |
DOIs | |
State | Published - Oct 1 2022 |
Keywords
- Design and human factors
- gesture, posture and facial expressions
- human-robot collaboration
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