Abstract
These days, low-cost commercial drones are rapidly used in variety of fields, ranging from manufacturing and logistics to STEM education. This paper addresses a fully-actuated Tello Drone’s control under random positions and in an indoor environment. For this purpose, we designed a closed-loop controller based on computer vision techniques for face recognition and a gesticulation rule for navigable motion. To be more detailed, Local Binary Pattern Histogram combined with SQLite 3 is initially applied to detect the right target. After that, the built-in controller determines the hand gesture on the human target to transmit the commands to Tello Drone. Additionally, the user interface is also developed to summary and display the drone info during operation process. The experimental results revealed the effectiveness of the suggested strategy and the reliability of the drone under different scenarios.
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Bui, HA., Nguyen, AT., Nguyen, TH., Nguyen, XT. (2024). Face Recognition and Hand Gesture Control for Tello Drone Navigation. In: Long, B.T., et al. Proceedings of the 3rd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2022). MMMS 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-57460-3_45
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DOI: https://doi.org/10.1007/978-3-031-57460-3_45
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