Vision-Based System Identification of a Quadrotor

TL;DR

This paper demonstrates the use of onboard vision systems for system identification in quadrotors, improving modeling accuracy and control performance through grey-box modeling and LQR controller design.

Contribution

It introduces a vision-based system identification approach for quadrotors, addressing modeling uncertainties and validating its effectiveness for control applications.

Findings

Vision-based identification yields consistent model performance.

Grey-box modeling reduces uncertainties in thrust and drag coefficients.

Onboard vision systems enhance quadrotor control accuracy.

Abstract

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.