Vision-Based System Identification of a Quadrotor
This incremental work addresses quadrotor control challenges for robotics and drone applications, potentially improving performance and enabling fault detection.
The paper tackled quadrotor modeling complexities, particularly thrust and drag coefficients, by using vision-based system identification and an LQR controller, demonstrating consistent performance between models to validate the approach.
This paper explores the application of vision-based system identification techniques in quadrotor modeling and control. Through experiments and analysis, we address the complexities and limitations of quadrotor modeling, particularly in relation to thrust and drag coefficients. Grey-box modeling is employed to mitigate uncertainties, and the effectiveness of an onboard vision system is evaluated. An LQR controller is designed based on a system identification model using data from the onboard vision system. The results demonstrate consistent performance between the models, validating the efficacy of vision based system identification. This study highlights the potential of vision-based techniques in enhancing quadrotor modeling and control, contributing to improved performance and operational capabilities. Our findings provide insights into the usability and consistency of these techniques, paving the way for future research in quadrotor performance enhancement, fault detection, and decision-making processes.