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논문 기본 정보

자료유형
학술대회자료
저자정보
Saewoong Min (Korea Advanced Institute of Science and Technology (KAIST)) Minjae Kang (Korea Advanced Institute of Science and Technology (KAIST)) Eunji Kim (Korea Advanced Institute of Science and Technology (KAIST)) Chulwoo Rhim (Korea Advanced Institute of Science and Technology (KAIST))
저널정보
제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2023
발행연도
2023.10
수록면
732 - 737 (6page)

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This study aims to develop an Artificial Neural Network (ANN) based model to predict the tilt angle of Unmanned Aerial Vehicles (UAVs) by considering external forces such as wind speed. With the increasing commercialization of self-driving UAVs for various applications, including shipping, cleaning, and delivery, ensuring their safe and efficient operation is crucial. GPS signals are not always reliable during autonomous flight, which can lead to collisions or accidents. The proposed ANN model aims to reduce the cost and complexity of UAV control systems while maintaining high prediction accuracy (R2 values of 0.8592, 0.8722, and 0.9678). By using data sets obtained from current and past states, the model can predict future states and stably control the UAV without deviating from its path. Although this study focuses on wind speed as the primary external force, incorporating additional data sources such as gyro sensors, temperature, barometric pressure, and image data could further enhance the accuracy of tilt angle predictions and self-flying control. The proposed model has the potential to be applied to various UAVs and provide critical information for decision-making in autonomous flight.

목차

Abstract
1. INTRODUCTION
2. METHODOLOGY
3. RESULT
4. CONCLUSIONS
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