Search for: All records

We propose an implementation of an LQR con- troller for the full-state tracking of a time-dependent trajectory with a multirotor UAV. The proposed LQR formulation is based in Lie theory and linearized at each time step according to the multirotor’s current state. We show experiments in both simulation and hardware that demonstrate the proposed control scheme’s ability to accurately reach and track a given trajectory. The implementation is shown to run onboard at the full rate of a UAV’s estimated state. 

This paper presents a method of tracking multiple ground targets from an unmanned aerial vehicle (UAV) in a 3D reference frame. The tracking method uses a monocular camera and makes no assumptions on the shape of the terrain or the target motion. The UAV runs two cascaded estimators. The first is an Extended Kalman Filter (EKF), which is responsible for tracking the UAV’s state, such as position and velocity relative to a fixed frame. The second estimator is an EKF that is responsible for estimating a fixed number of landmarks within the camera’s field of view. Landmarks are parameterized by a quaternion associated with bearing from the camera’s optical axis and an inverse distance parameter. The bearing quaternion allows for a minimal representation of each landmark’s direction and distance, a filter with no singularities, and a fast update rate due to few trigonometric functions. Three methods for estimating the ground target positions are demonstrated: the first uses the landmark estimator directly on the targets, the second computes the target depth with a weighted average of converged landmark depths, and the third extends the target’s measured bearing vector to intersect a ground plane approximated from the landmark estimates. Simulation results show that the third target estimation method yields the most accurate results.