Enhancing Drone Navigation and Control: Gesture-Based Piloting, Obstacle Avoidance, and 3D Trajectory Mapping
Autonomous drone navigation presents challenges for users unfamiliar with manual flight controls, increasing the risk of collisions. This research addresses this issue by developing a multifunctional drone control system that integrates hand gesture recognition, obstacle avoidance, and 3D mapping to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/13/7340 |
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| author | Ben Taylor Mathew Allen Preston Henson Xu Gao Haroon Malik Pingping Zhu |
| author_facet | Ben Taylor Mathew Allen Preston Henson Xu Gao Haroon Malik Pingping Zhu |
| author_sort | Ben Taylor |
| collection | DOAJ |
| description | Autonomous drone navigation presents challenges for users unfamiliar with manual flight controls, increasing the risk of collisions. This research addresses this issue by developing a multifunctional drone control system that integrates hand gesture recognition, obstacle avoidance, and 3D mapping to improve accessibility and safety. The system utilizes Google’s MediaPipe Hands software library, which employs machine learning to track 21 key landmarks of the user’s hand, enabling gesture-based control of the drone. Each recognized gesture is mapped to a flight command, eliminating the need for a traditional controller. The obstacle avoidance system, utilizing the Flow Deck V2 and Multi-Ranger Deck, detects objects within a safety threshold and autonomously moves the drone by a predefined avoidance distance away to prevent collisions. A mapping system continuously logs the drone’s flight path and detects obstacles, enabling 3D visualization of drone’s trajectory after the drone landing. Also, an AI-Deck streams live video, enabling navigation beyond the user’s direct line of sight. Experimental validation with the Crazyflie drone demonstrates seamless integration of these systems, providing a beginner-friendly experience where users can fly drones safely without prior expertise. This research enhances human–drone interaction, making drone technology more accessible for education, training, and intuitive navigation. |
| format | Article |
| id | doaj-art-e28ce576916f43118b3d95c2a197c83e |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e28ce576916f43118b3d95c2a197c83e2025-08-20T03:50:16ZengMDPI AGApplied Sciences2076-34172025-06-011513734010.3390/app15137340Enhancing Drone Navigation and Control: Gesture-Based Piloting, Obstacle Avoidance, and 3D Trajectory MappingBen Taylor0Mathew Allen1Preston Henson2Xu Gao3Haroon Malik4Pingping Zhu5Department of Computer Sciences and Electrical Engineering, Marshall University, Huntington, WV 25755, USADepartment of Computer Sciences and Electrical Engineering, Marshall University, Huntington, WV 25755, USADepartment of Computer Sciences and Electrical Engineering, Marshall University, Huntington, WV 25755, USADepartment of Computer Sciences and Electrical Engineering, Marshall University, Huntington, WV 25755, USADepartment of Computer Sciences and Electrical Engineering, Marshall University, Huntington, WV 25755, USADepartment of Computer Sciences and Electrical Engineering, Marshall University, Huntington, WV 25755, USAAutonomous drone navigation presents challenges for users unfamiliar with manual flight controls, increasing the risk of collisions. This research addresses this issue by developing a multifunctional drone control system that integrates hand gesture recognition, obstacle avoidance, and 3D mapping to improve accessibility and safety. The system utilizes Google’s MediaPipe Hands software library, which employs machine learning to track 21 key landmarks of the user’s hand, enabling gesture-based control of the drone. Each recognized gesture is mapped to a flight command, eliminating the need for a traditional controller. The obstacle avoidance system, utilizing the Flow Deck V2 and Multi-Ranger Deck, detects objects within a safety threshold and autonomously moves the drone by a predefined avoidance distance away to prevent collisions. A mapping system continuously logs the drone’s flight path and detects obstacles, enabling 3D visualization of drone’s trajectory after the drone landing. Also, an AI-Deck streams live video, enabling navigation beyond the user’s direct line of sight. Experimental validation with the Crazyflie drone demonstrates seamless integration of these systems, providing a beginner-friendly experience where users can fly drones safely without prior expertise. This research enhances human–drone interaction, making drone technology more accessible for education, training, and intuitive navigation.https://www.mdpi.com/2076-3417/15/13/7340gesture-based drone controlautonomous obstacle avoidancehuman–drone interaction3D mapping and navigation |
| spellingShingle | Ben Taylor Mathew Allen Preston Henson Xu Gao Haroon Malik Pingping Zhu Enhancing Drone Navigation and Control: Gesture-Based Piloting, Obstacle Avoidance, and 3D Trajectory Mapping Applied Sciences gesture-based drone control autonomous obstacle avoidance human–drone interaction 3D mapping and navigation |
| title | Enhancing Drone Navigation and Control: Gesture-Based Piloting, Obstacle Avoidance, and 3D Trajectory Mapping |
| title_full | Enhancing Drone Navigation and Control: Gesture-Based Piloting, Obstacle Avoidance, and 3D Trajectory Mapping |
| title_fullStr | Enhancing Drone Navigation and Control: Gesture-Based Piloting, Obstacle Avoidance, and 3D Trajectory Mapping |
| title_full_unstemmed | Enhancing Drone Navigation and Control: Gesture-Based Piloting, Obstacle Avoidance, and 3D Trajectory Mapping |
| title_short | Enhancing Drone Navigation and Control: Gesture-Based Piloting, Obstacle Avoidance, and 3D Trajectory Mapping |
| title_sort | enhancing drone navigation and control gesture based piloting obstacle avoidance and 3d trajectory mapping |
| topic | gesture-based drone control autonomous obstacle avoidance human–drone interaction 3D mapping and navigation |
| url | https://www.mdpi.com/2076-3417/15/13/7340 |
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