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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.
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UAVs Control Using 3D Hand Keypoint Gestures
Over past few years, unmanned aircraft vehicles (UAVs) have been becoming more and more popular for various purposes such as surveillance, automated industry, robotics, vehicle guidance, traffic monitoring and control system. It is very important to have multiple methods of UAVs controlling to fit in UAVs usages. The goal of this work was to develop a new technique to control an UAV by using different hand gestures. To achieve this, a hand keypoint detection algorithm was used to detect 21 keypoints in the hand. Then this keypoints were used as the input to an intelligent system based on Convolutional Neural Networks (CNN) that was able to classify the hand gestures. To capture the hand gestures, the video camera of the UAV was used. A database containing 2400 hand images was created and used to train the CNN. The database contained 8 different hand gestures that were selected to send specific motion commands to the UAV. The accuracy of the CNN to classify the hand gestures was 93%. To test the capabilities of our intelligent control system, a small UAV, the DJI Ryze Tello drone, was used. The experimental results demonstrated that the DJI Tello drone was able to be successfully controlled by hand gestures in real time.
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- Award ID(s):
- 1950207
- PAR ID:
- 10327233
- Editor(s):
- IEEE
- Date Published:
- Journal Name:
- SoutheastCon 2022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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