Towards Robust Onboard Control for Quadrotors via Ultra-Wideband-based Localization

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Abstract

This paper describes an indoor navigation approach using estimation and control for horizontal translational motion and heading angle for quadrotor Unmanned Aerial Vehicles (UAVs) via Ultra-Wideband (UWB)-based localization. In particular, to cope with noisy measurements, emanating from model uncertainties, and Non-Line-Of-Sight (NLOS) conditions, a Linear Quadratic Regulator (LQR) is deployed along with a Maximum Correntropy Criterion Kalman Filter (MCC-KF). This approach has proven improved robustness compared to the traditional Kalman Filter (KF) against non-Gaussian noise. A testbed with a quadrotor was developed for evaluating the performance of our proposed approach. We demonstrate, via the experimental setup, that the MCC-KF outperforms the use of KF in the presence of shots of mixed noise and communication delays, enabling onboard robust estimation and control via UWB-based localization.

Publication
In 16th International Wireless Communications and Mobile Computing (IWCMC)
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Evagoras Makridis
Evagoras Makridis
PhD Student in Distributed Decision and Control of Networked Systems

My research interests include autonomous systems and more specifically in networked control systems, and data-driven sequential decision-making (Reinforcement Learning), with applications in quadrotor navigation, resource management, and wireless link adaptation and scheduling.