Robust state estimation for Micro Aerial Vehicles based on system dynamics

Abstract

In this work, we present a model-based estimation scheme for multi-rotor Micro Aerial Vehicles (MAVs). Although modeling approaches for MAVs have been presented in the past, these models have rarely been used for real-time state estimation onboard MAVs. Building on this work, we identify the most dominant effects and propose an easy-to-use calibration scheme for estimation of the model parameters. Given the calibration estimates for these parameters, we derive a state estimator where the state prediction of the indirect Extended Kalman Filter (EKF) is driven by a MAV model. Solely using measurements from the Inertial Measurement Unit (IMU) and a barometric pressure sensor -both available on almost every MAV -our model-based formulation keeps the estimated velocity of the MAV bounded in all directions, as opposed to state of the art IMU-model driven state estimators onboard MAVs. This is crucial for keeping MAVs airborne safely, for instance in the case of failures or re-initialization of vision based localization systems.

Differential Flatness of Quadrotor Dynamics Subject to Rotor Drag for Accurate Tracking of High-Speed Trajectories

“…From this, (7) and (8), and the constraints that x B , y B , and z B must be orthogonal to each other and of unit length, we can construct R with…”

“…where b is an arbitrary vector. The derivative of the orientation iṡ R = R ·ω (8) and the derivative of the norm of a vector b can be written as…”

Maximum likelihood parameter identification for MAVs

“…The only possibility to distinguish them are the coupling terms in the angular acceleration (2). For the datasets this means simultaneous rotations around the z-axis and the x-or y-axis are required.…”

“…This leads to having highly unstable platforms, whose control requires accurate models. Additionally, our previous work focuses on incorporating the dynamic model directly in a state estimator to be able to recover from pose sensor outages [2], such as in the case of vision failures or flying out of the range of external sensing systems.…”

Sampling-based motion planning for active multirotor system identification

“…During aggressive maneuvers a nonlinear model can help compensate for significant aerodynamic effects that impact the vehicle [2]. In manipulation or transportation, where the external forces change regularly, effective models can be used to estimate these disturbances [3]. Furthermore, one can use a system model to create a realistic simulation environment [4], avoiding the need to carry out expensive real-world experiments.…”