Maximum likelihood parameter identification for MAVs

Abstract

As the applications of Micro Aerial Vehicles (MAVs) get more and more complex, and require highly dynamic motions, it becomes essential to have an accurate dynamic model of the MAV. Such a model can be used for reliable state estimation, control, and for realistic simulation. A good model requires accurate estimates of physical parameters of the system, which we aim to estimate from recorded flight data. In this paper, we present a detailed physical model of the MAV and a maximum likelihood estimation scheme for determining the dominant parameters, such as inertia matrix, center of gravity (CoG) with respect to the IMU, and parameters related to the aerodynamics. To incorporate all information given by the IMU and the physical MAV model, we propose to use two process models in the optimization. We show the effectiveness of the method on simulated data, as well as on a real platform.

Robust collaborative object transportation using multiple MAVs

“…The scalar k drag lumps together the aerodynamic drag force of the body and the aerodynamic forces due to the blade flapping. The MAV model parameters can be estimated using the method proposed by Burri et al (2016).…”

Robust collision avoidance for multiple micro aerial vehicles using nonlinear model predictive control

“…The importance of these effects stems from the fact that they introduce additional forces in the x − y rotor plane, adding some damping to the MAV velocity as shown in [15]. It is possible to combine these effects as shown in [16], [17] into one lumped drag coefficient k D .…”

Dynamic System Identification, and Control for a Cost-Effective and Open-Source Multi-rotor MAV

“…The data is subscribed by the Multi-Sensor Fusion (MSF) framework [8] to filter noisy measurement and to compensate for possible delay in the WiFi network connection. The ground station sets either a goal position as denoted [p * , q * ] for position and orientation or N sequences, [p * 1:N , q * 1:N ], generated by the trajectory generator [17].…”