2019/06/22 by Suwen Gu, Gu, Suwen, Menghao Lin +1
Computer Science · Engineering · #Aerospace and Aviation Technology #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Robotics (cs.RO) #Structural Health Monitoring Techniques #cs.RO
paper · pdf · doi:10.48550/arxiv.1906.09371
Captone Project
arxiv created 2019/06/22 · openalex publication_date 2019/06/22 · arxiv updated 2019/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose the use of basic inertial measurement units (IMU) which contain sensors such as accelerometers and gyroscopes already on-board drones to detect the speed and direction of wind gusts. The ability to quickly sense wind gusts has many applications, the most notable of which is in flight assistance of the drone, where it may adjust motor power parameter to compensate for such external factors or steer the drone toward a safer direction. To illustrate the feasibility of the approach, we conducted studies to assess how reliably wind speed and wind direction can be detected while a quad-copter drone is hovering and then in-motion, using off-the-shelf classifiers. Empirical results with real-life data, collected on a micro aerial vehicle (MAV) in a physical room with a consumer-grade fan, show that (1.1) wind speed can be detected with high accuracy after training, not only on the same drone, but also across different drones of the same class, while (1.2) wind direction can be detected with high accuracy after training on the same drone, but with limited generalizability to other drones. (1.3) We demonstrate how real-time detection of wind speed, using offline trained models, is feasible and can be done with high accuracy. (2.1) Finally, we find the reason behind the lower accuracy for wind direction detection during the analysis of drones in-motion.