Free Energy Principle for State and Input Estimation of a Quadcopter Flying in Wind

TL;DR

This paper demonstrates that the Dynamic Expectation Maximization (DEM) algorithm, inspired by the free energy principle, effectively estimates states and inputs of a quadcopter in wind, outperforming traditional methods in noisy conditions.

Contribution

It provides the first experimental validation of DEM as a practical state and input estimator for real robots under wind disturbances.

Findings

DEM achieves minimal estimation error compared to benchmarks.

DEM performs similarly to UIO in input estimation.

Prior beliefs influence the accuracy and complexity of DEM estimates.

Abstract

The free energy principle from neuroscience provides a brain-inspired perception scheme through a data-driven model learning algorithm called Dynamic Expectation Maximization (DEM). This paper aims at introducing an experimental design to provide the first experimental confirmation of the usefulness of DEM as a state and input estimator for real robots. Through a series of quadcopter flight experiments under unmodelled wind dynamics, we prove that DEM can leverage the information from colored noise for accurate state and input estimation through the use of generalized coordinates. We demonstrate the superior performance of DEM for state estimation under colored noise with respect to other benchmarks like State Augmentation, SMIKF and Kalman Filtering through its minimal estimation error. We demonstrate the similarities in the performance of DEM and Unknown Input Observer (UIO) for input estimation. The paper concludes by showing the influence of prior beliefs in shaping the accuracy-complexity trade-off during DEM's estimation.