Backflipping with Miniature Quadcopters by Gaussian Process Based Control and Planning

TL;DR

This paper introduces two innovative control and planning methods for executing backflip maneuvers with miniature quadcopters, utilizing Gaussian Process models for optimization, robustness, and trajectory planning, validated through simulations and real-world experiments.

Contribution

It presents novel Gaussian Process-based control and planning techniques for quadcopter flips, improving reliability and efficiency over existing methods.

Findings

Bayesian optimization effectively finds optimal motion primitives.

The robust adaptive controller maintains tracking despite model uncertainties.

Trajectory planning with quadratic programming is feasible for flip maneuvers.

Abstract

The paper proposes two control methods for performing a backflip maneuver with miniature quadcopters. First, an existing feedforward control approach is improved by finding the optimal sequence of motion primitives via Bayesian optimization, using a surrogate Gaussian Process model. To evaluate the cost function, the flip maneuver is performed repeatedly in a simulation environment. The second method is based on closed-loop control and it consists of two main steps: first a novel robust, adaptive controller is designed to provide reliable reference tracking even in case of model uncertainties. The controller is constructed by augmenting the nominal model of the drone with a Gaussian Process that is trained by using measurement data. Second, an efficient trajectory planning algorithm is proposed, which designs feasible trajectories for the flip maneuver by using only quadratic programming. The two approaches are analyzed in simulations and in real experiments using Bitcraze Crazyflie 2.1 quadcopters.