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


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.

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

My research interests include autonomous systems in networks, distributed optimization, and data-driven sequential decision-making (Reinforcement Learning), with applications in quadrotor navigation, and resource management.