AR.Drone 2.0 state estimation using Dynamic Expectation Maximization

Abstract

Recent developments in neuroscience research, mainly introduced by neuroscientist Karl J. Friston, have resulted in a concept called the Free Energy Principle (FEP). The FEP is a brain theory unifying action, perception and learning. An important observation is that autonomous robots have to perform similar tasks to the human brain. Therefore, these developments are very interesting from a control engineering perspective. A lot of research is going on in the direction of Active Inference (AI), which includes the perception and action parts of the FEP used for filtering and control on physical systems. However, a research gap exists for the theory describing the complex mathematical backbone of perception and learning, called Dynamic Expectation Maximization (DEM). DEM is a parameter estimation algorithm that can be used to perform filtering as well as system identification. This thesis is part of the research aiming to evaluate the performance of DEM as a filtering as well as system identification technique. To be more specific, this thesis considers the filtering part. In general, a filter is meant to derive the states of a system using a system model and sensor measurements. The system model is often not perfect, resulting in process noise, and the sensors provide noisy data, resulting in measurement noise. Many existing filtering techniques, including the conventional Kalman filter, assume these noises to be white. However, the process noise contains unmodelled system dynamics that introduce correlation. Therefore, by definition, the process noise is not white, but coloured. The main advantage of using the DEM filter is the ability to extract information from the correlation in the measurements and the noises to construct a better state estimate. Simulation results in previous work indicated the potential of DEM as a filter. The next step is to prove the usefulness of the DEM filter on a physical system. In order to do so, this thesis describes the design of an experimental setup with a quadrotor Unmanned Aerial Vehicle (UAV) used to evaluate the performance of the DEM filter. The recorded flight data is used to analyze the process and measurement noises. Furthermore, the DEM filter is compared with the conventional Kalman filter. It turns out that for a relatively big subspace of DEM tuning parameters, the DEM filter is able to outperform the Kalman filter, which proves the potential of this filtering algorithm in a practical robotics setting.