Vision-Based System Identification of a Quadrotor

2023-07-27

International Conference on Image, Vision and Computing

AI Summary

High model uncertainty in quadcopter dynamics—stemming from inaccurate calibration of thrust and drag coefficients—severely limits control performance and autonomous decision-making. Method: This paper proposes a novel onboard-vision-based system identification paradigm. Real-time pose and motion features are captured via visual sensors and integrated into a gray-box modeling framework that explicitly embeds physical constraints to mitigate parameter coupling. This significantly improves the identification accuracy of critical aerodynamic parameters, including thrust and drag coefficients. An LQR controller is subsequently designed based on the identified model and validated in closed-loop experiments. Results: Compared to conventional black-box or purely first-principles models, the proposed approach achieves superior model fidelity and enhanced robustness in control response. It establishes a high-confidence dynamical foundation for fault detection and autonomous flight, enabling more reliable real-time decision-making and control synthesis.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Low Level & Physics-based Vision

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Addressing quadrotor modeling complexities in thrust and drag coefficients
• Employing grey-box modeling to mitigate system uncertainties
• Evaluating onboard vision system effectiveness for system identification

Innovation

• Uses onboard vision system for data collection
• Applies grey-box modeling to reduce uncertainties
• Implements LQR controller from identified vision model